Overview

Dataset statistics

Number of variables31
Number of observations96082
Missing cells399799
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.7 MiB
Average record size in memory248.0 B

Variable types

Categorical23
Numeric8

Alerts

CIVILIAN_TARGETING has constant value ""Constant
EVENT_ID_CNTY has a high cardinality: 96082 distinct valuesHigh cardinality
EVENT_DATE has a high cardinality: 1901 distinct valuesHigh cardinality
ACTOR1 has a high cardinality: 115 distinct valuesHigh cardinality
ASSOC_ACTOR_1 has a high cardinality: 691 distinct valuesHigh cardinality
ACTOR2 has a high cardinality: 88 distinct valuesHigh cardinality
ASSOC_ACTOR_2 has a high cardinality: 378 distinct valuesHigh cardinality
ADMIN2 has a high cardinality: 150 distinct valuesHigh cardinality
ADMIN3 has a high cardinality: 776 distinct valuesHigh cardinality
LOCATION has a high cardinality: 2470 distinct valuesHigh cardinality
SOURCE has a high cardinality: 4819 distinct valuesHigh cardinality
NOTES has a high cardinality: 95577 distinct valuesHigh cardinality
TAGS has a high cardinality: 358 distinct valuesHigh cardinality
YEAR is highly overall correlated with INTER1 and 2 other fieldsHigh correlation
INTER1 is highly overall correlated with YEAR and 6 other fieldsHigh correlation
INTER2 is highly overall correlated with ACTOR2High correlation
INTERACTION is highly overall correlated with YEAR and 7 other fieldsHigh correlation
LATITUDE is highly overall correlated with ISO and 3 other fieldsHigh correlation
LONGITUDE is highly overall correlated with INTERACTION and 1 other fieldsHigh correlation
TIMESTAMP is highly overall correlated with YEAR and 2 other fieldsHigh correlation
DISORDER_TYPE is highly overall correlated with INTER1 and 4 other fieldsHigh correlation
EVENT_TYPE is highly overall correlated with INTER1 and 4 other fieldsHigh correlation
SUB_EVENT_TYPE is highly overall correlated with INTER1 and 3 other fieldsHigh correlation
ACTOR2 is highly overall correlated with INTER1 and 8 other fieldsHigh correlation
ISO is highly overall correlated with LATITUDE and 4 other fieldsHigh correlation
REGION is highly overall correlated with LATITUDE and 4 other fieldsHigh correlation
COUNTRY is highly overall correlated with LATITUDE and 4 other fieldsHigh correlation
ADMIN1 is highly overall correlated with LATITUDE and 4 other fieldsHigh correlation
GEO_PRECISION is highly overall correlated with ACTOR2High correlation
TIME_PRECISION is highly imbalanced (94.6%)Imbalance
DISORDER_TYPE is highly imbalanced (75.2%)Imbalance
SUB_EVENT_TYPE is highly imbalanced (60.7%)Imbalance
ACTOR1 is highly imbalanced (63.4%)Imbalance
ACTOR2 is highly imbalanced (63.3%)Imbalance
ASSOC_ACTOR_2 is highly imbalanced (72.3%)Imbalance
ISO is highly imbalanced (99.9%)Imbalance
REGION is highly imbalanced (99.9%)Imbalance
COUNTRY is highly imbalanced (99.9%)Imbalance
ADMIN1 is highly imbalanced (50.7%)Imbalance
SOURCE is highly imbalanced (58.6%)Imbalance
SOURCE_SCALE is highly imbalanced (64.8%)Imbalance
TAGS is highly imbalanced (56.5%)Imbalance
ASSOC_ACTOR_1 has 89594 (93.2%) missing valuesMissing
ACTOR2 has 44253 (46.1%) missing valuesMissing
ASSOC_ACTOR_2 has 81404 (84.7%) missing valuesMissing
CIVILIAN_TARGETING has 91894 (95.6%) missing valuesMissing
ADMIN3 has 2402 (2.5%) missing valuesMissing
TAGS has 90144 (93.8%) missing valuesMissing
FATALITIES is highly skewed (γ1 = 43.39440215)Skewed
EVENT_ID_CNTY is uniformly distributedUniform
NOTES is uniformly distributedUniform
EVENT_ID_CNTY has unique valuesUnique
INTER2 has 44253 (46.1%) zerosZeros
FATALITIES has 91308 (95.0%) zerosZeros

Reproduction

Analysis started2023-03-29 12:44:50.517105
Analysis finished2023-03-29 12:45:15.929977
Duration25.41 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

EVENT_ID_CNTY
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct96082
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
ROU448
 
1
UKR63701
 
1
UKR63655
 
1
UKR63686
 
1
UKR63648
 
1
Other values (96077)
96077 

Length

Max length8
Median length8
Mean length7.8864095
Min length4

Characters and Unicode

Total characters757742
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96082 ?
Unique (%)100.0%

Sample

1st rowROU448
2nd rowROU1885
3rd rowROU1940
4th rowROU1945
5th rowROU1947

Common Values

ValueCountFrequency (%)
ROU448 1
 
< 0.1%
UKR63701 1
 
< 0.1%
UKR63655 1
 
< 0.1%
UKR63686 1
 
< 0.1%
UKR63648 1
 
< 0.1%
UKR63722 1
 
< 0.1%
UKR63647 1
 
< 0.1%
UKR63719 1
 
< 0.1%
UKR63671 1
 
< 0.1%
UKR63645 1
 
< 0.1%
Other values (96072) 96072
> 99.9%

Length

2023-03-29T18:15:16.043258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rou448 1
 
< 0.1%
tur25961 1
 
< 0.1%
rou1947 1
 
< 0.1%
rou1961 1
 
< 0.1%
rou2026 1
 
< 0.1%
rou2045 1
 
< 0.1%
tur14260 1
 
< 0.1%
tur18570 1
 
< 0.1%
tur21094 1
 
< 0.1%
ukr6 1
 
< 0.1%
Other values (96072) 96072
> 99.9%

Most occurring characters

ValueCountFrequency (%)
R 96082
12.7%
U 96082
12.7%
K 96067
12.7%
3 48836
 
6.4%
2 48820
 
6.4%
1 48792
 
6.4%
5 48772
 
6.4%
4 48759
 
6.4%
6 48111
 
6.3%
8 47752
 
6.3%
Other values (5) 129669
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 469496
62.0%
Uppercase Letter 288246
38.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 48836
10.4%
2 48820
10.4%
1 48792
10.4%
5 48772
10.4%
4 48759
10.4%
6 48111
10.2%
8 47752
10.2%
7 47733
10.2%
9 44148
9.4%
0 37773
8.0%
Uppercase Letter
ValueCountFrequency (%)
R 96082
33.3%
U 96082
33.3%
K 96067
33.3%
O 8
 
< 0.1%
T 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 469496
62.0%
Latin 288246
38.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 48836
10.4%
2 48820
10.4%
1 48792
10.4%
5 48772
10.4%
4 48759
10.4%
6 48111
10.2%
8 47752
10.2%
7 47733
10.2%
9 44148
9.4%
0 37773
8.0%
Latin
ValueCountFrequency (%)
R 96082
33.3%
U 96082
33.3%
K 96067
33.3%
O 8
 
< 0.1%
T 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 96082
12.7%
U 96082
12.7%
K 96067
12.7%
3 48836
 
6.4%
2 48820
 
6.4%
1 48792
 
6.4%
5 48772
 
6.4%
4 48759
 
6.4%
6 48111
 
6.3%
8 47752
 
6.3%
Other values (5) 129669
17.1%

EVENT_DATE
Categorical

Distinct1901
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
03-August-2022
 
215
07-September-2022
 
212
08-September-2022
 
208
18-August-2022
 
194
28-July-2022
 
190
Other values (1896)
95063 

Length

Max length17
Median length15
Mean length14.213058
Min length11

Characters and Unicode

Total characters1365619
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-May-2019
2nd row28-March-2022
3rd row28-July-2022
4th row31-July-2022
5th row04-August-2022

Common Values

ValueCountFrequency (%)
03-August-2022 215
 
0.2%
07-September-2022 212
 
0.2%
08-September-2022 208
 
0.2%
18-August-2022 194
 
0.2%
28-July-2022 190
 
0.2%
21-September-2022 190
 
0.2%
08-February-2023 189
 
0.2%
01-March-2023 189
 
0.2%
01-September-2022 188
 
0.2%
14-July-2022 184
 
0.2%
Other values (1891) 94123
98.0%

Length

2023-03-29T18:15:16.180481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03-august-2022 215
 
0.2%
07-september-2022 212
 
0.2%
08-september-2022 208
 
0.2%
18-august-2022 194
 
0.2%
28-july-2022 190
 
0.2%
21-september-2022 190
 
0.2%
08-february-2023 189
 
0.2%
01-march-2023 189
 
0.2%
01-september-2022 188
 
0.2%
14-july-2022 184
 
0.2%
Other values (1891) 94123
98.0%

Most occurring characters

ValueCountFrequency (%)
2 237078
17.4%
- 192164
14.1%
0 143711
 
10.5%
e 88029
 
6.4%
1 84036
 
6.2%
r 74751
 
5.5%
u 47799
 
3.5%
a 42338
 
3.1%
b 41442
 
3.0%
y 33215
 
2.4%
Other values (27) 381056
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 576492
42.2%
Lowercase Letter 500881
36.7%
Dash Punctuation 192164
 
14.1%
Uppercase Letter 96082
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 88029
17.6%
r 74751
14.9%
u 47799
9.5%
a 42338
8.5%
b 41442
8.3%
y 33215
 
6.6%
c 24747
 
4.9%
t 23973
 
4.8%
m 23754
 
4.7%
o 16555
 
3.3%
Other values (8) 84278
16.8%
Decimal Number
ValueCountFrequency (%)
2 237078
41.1%
0 143711
24.9%
1 84036
 
14.6%
9 25526
 
4.4%
8 24904
 
4.3%
3 22984
 
4.0%
5 9700
 
1.7%
4 9692
 
1.7%
7 9549
 
1.7%
6 9312
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
J 23323
24.3%
M 17062
17.8%
A 14255
14.8%
F 9430
9.8%
N 8297
 
8.6%
O 8258
 
8.6%
S 8192
 
8.5%
D 7265
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 192164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 768656
56.3%
Latin 596963
43.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 88029
14.7%
r 74751
12.5%
u 47799
 
8.0%
a 42338
 
7.1%
b 41442
 
6.9%
y 33215
 
5.6%
c 24747
 
4.1%
t 23973
 
4.0%
m 23754
 
4.0%
J 23323
 
3.9%
Other values (16) 173592
29.1%
Common
ValueCountFrequency (%)
2 237078
30.8%
- 192164
25.0%
0 143711
18.7%
1 84036
 
10.9%
9 25526
 
3.3%
8 24904
 
3.2%
3 22984
 
3.0%
5 9700
 
1.3%
4 9692
 
1.3%
7 9549
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1365619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 237078
17.4%
- 192164
14.1%
0 143711
 
10.5%
e 88029
 
6.4%
1 84036
 
6.2%
r 74751
 
5.5%
u 47799
 
3.5%
a 42338
 
3.1%
b 41442
 
3.0%
y 33215
 
2.4%
Other values (27) 381056
27.9%

YEAR
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.6526
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:16.301972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2021
Q32022
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6932727
Coefficient of variation (CV)0.00083798308
Kurtosis-1.37338
Mean2020.6526
Median Absolute Deviation (MAD)1
Skewness-0.32017167
Sum1.9414834 × 108
Variance2.8671724
MonotonicityNot monotonic
2023-03-29T18:15:16.413970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 36303
37.8%
2019 16557
17.2%
2018 15191
15.8%
2020 9938
 
10.3%
2023 9472
 
9.9%
2021 8621
 
9.0%
ValueCountFrequency (%)
2018 15191
15.8%
2019 16557
17.2%
2020 9938
 
10.3%
2021 8621
 
9.0%
2022 36303
37.8%
2023 9472
 
9.9%
ValueCountFrequency (%)
2023 9472
 
9.9%
2022 36303
37.8%
2021 8621
 
9.0%
2020 9938
 
10.3%
2019 16557
17.2%
2018 15191
15.8%

TIME_PRECISION
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
1
95142 
2
 
779
3
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96082
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

Length

2023-03-29T18:15:16.530581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:16.654169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96082
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 96082
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 95142
99.0%
2 779
 
0.8%
3 161
 
0.2%

DISORDER_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Political violence
87884 
Demonstrations
 
5799
Strategic developments
 
2381
Political violence; Demonstrations
 
18

Length

Max length34
Median length18
Mean length17.860702
Min length14

Characters and Unicode

Total characters1716092
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolitical violence
2nd rowStrategic developments
3rd rowDemonstrations
4th rowStrategic developments
5th rowDemonstrations

Common Values

ValueCountFrequency (%)
Political violence 87884
91.5%
Demonstrations 5799
 
6.0%
Strategic developments 2381
 
2.5%
Political violence; Demonstrations 18
 
< 0.1%

Length

2023-03-29T18:15:16.764614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:16.887784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
political 87902
47.2%
violence 87902
47.2%
demonstrations 5817
 
3.1%
strategic 2381
 
1.3%
developments 2381
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i 271904
15.8%
l 266087
15.5%
e 191145
11.1%
o 189819
11.1%
c 178185
10.4%
t 106679
 
6.2%
n 101917
 
5.9%
a 96100
 
5.6%
90301
 
5.3%
v 90283
 
5.3%
Other values (10) 133672
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1529673
89.1%
Uppercase Letter 96100
 
5.6%
Space Separator 90301
 
5.3%
Other Punctuation 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 271904
17.8%
l 266087
17.4%
e 191145
12.5%
o 189819
12.4%
c 178185
11.6%
t 106679
 
7.0%
n 101917
 
6.7%
a 96100
 
6.3%
v 90283
 
5.9%
s 14015
 
0.9%
Other values (5) 23539
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
P 87902
91.5%
D 5817
 
6.1%
S 2381
 
2.5%
Space Separator
ValueCountFrequency (%)
90301
100.0%
Other Punctuation
ValueCountFrequency (%)
; 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1625773
94.7%
Common 90319
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 271904
16.7%
l 266087
16.4%
e 191145
11.8%
o 189819
11.7%
c 178185
11.0%
t 106679
 
6.6%
n 101917
 
6.3%
a 96100
 
5.9%
v 90283
 
5.6%
P 87902
 
5.4%
Other values (8) 45752
 
2.8%
Common
ValueCountFrequency (%)
90301
> 99.9%
; 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1716092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 271904
15.8%
l 266087
15.5%
e 191145
11.1%
o 189819
11.1%
c 178185
10.4%
t 106679
 
6.2%
n 101917
 
5.9%
a 96100
 
5.6%
90301
 
5.3%
v 90283
 
5.3%
Other values (10) 133672
7.8%

EVENT_TYPE
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Explosions/Remote violence
57393 
Battles
29227 
Protests
 
5581
Strategic developments
 
2381
Violence against civilians
 
1080

Length

Max length26
Median length26
Mean length18.983962
Min length5

Characters and Unicode

Total characters1824017
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowViolence against civilians
2nd rowStrategic developments
3rd rowProtests
4th rowStrategic developments
5th rowProtests

Common Values

ValueCountFrequency (%)
Explosions/Remote violence 57393
59.7%
Battles 29227
30.4%
Protests 5581
 
5.8%
Strategic developments 2381
 
2.5%
Violence against civilians 1080
 
1.1%
Riots 420
 
0.4%

Length

2023-03-29T18:15:17.144203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:17.260813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
violence 58473
37.0%
explosions/remote 57393
36.3%
battles 29227
18.5%
protests 5581
 
3.5%
strategic 2381
 
1.5%
developments 2381
 
1.5%
against 1080
 
0.7%
civilians 1080
 
0.7%
riots 420
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 276064
15.1%
o 239034
13.1%
s 160136
 
8.8%
l 148554
 
8.1%
t 135652
 
7.4%
i 122987
 
6.7%
n 120407
 
6.6%
61934
 
3.4%
c 61934
 
3.4%
v 60854
 
3.3%
Other values (14) 436461
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1551215
85.0%
Uppercase Letter 153475
 
8.4%
Space Separator 61934
 
3.4%
Other Punctuation 57393
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 276064
17.8%
o 239034
15.4%
s 160136
10.3%
l 148554
9.6%
t 135652
8.7%
i 122987
7.9%
n 120407
7.8%
c 61934
 
4.0%
v 60854
 
3.9%
p 59774
 
3.9%
Other values (6) 165819
10.7%
Uppercase Letter
ValueCountFrequency (%)
R 57813
37.7%
E 57393
37.4%
B 29227
19.0%
P 5581
 
3.6%
S 2381
 
1.6%
V 1080
 
0.7%
Space Separator
ValueCountFrequency (%)
61934
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 57393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1704690
93.5%
Common 119327
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 276064
16.2%
o 239034
14.0%
s 160136
9.4%
l 148554
8.7%
t 135652
8.0%
i 122987
 
7.2%
n 120407
 
7.1%
c 61934
 
3.6%
v 60854
 
3.6%
p 59774
 
3.5%
Other values (12) 319294
18.7%
Common
ValueCountFrequency (%)
61934
51.9%
/ 57393
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1824017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 276064
15.1%
o 239034
13.1%
s 160136
 
8.8%
l 148554
 
8.1%
t 135652
 
7.4%
i 122987
 
6.7%
n 120407
 
6.6%
61934
 
3.4%
c 61934
 
3.4%
v 60854
 
3.3%
Other values (14) 436461
23.9%

SUB_EVENT_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Shelling/artillery/missile attack
53997 
Armed clash
28667 
Peaceful protest
5427 
Air/drone strike
 
2497
Disrupted weapons use
 
1077
Other values (19)
 
4417

Length

Max length35
Median length33
Mean length24.348942
Min length5

Characters and Unicode

Total characters2339495
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttack
2nd rowDisrupted weapons use
3rd rowPeaceful protest
4th rowDisrupted weapons use
5th rowPeaceful protest

Common Values

ValueCountFrequency (%)
Shelling/artillery/missile attack 53997
56.2%
Armed clash 28667
29.8%
Peaceful protest 5427
 
5.6%
Air/drone strike 2497
 
2.6%
Disrupted weapons use 1077
 
1.1%
Remote explosive/landmine/IED 818
 
0.9%
Attack 719
 
0.7%
Looting/property destruction 389
 
0.4%
Other 377
 
0.4%
Abduction/forced disappearance 324
 
0.3%
Other values (14) 1790
 
1.9%

Length

2023-03-29T18:15:17.407507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
attack 54716
28.3%
shelling/artillery/missile 53997
27.9%
armed 28667
14.8%
clash 28667
14.8%
protest 5563
 
2.9%
peaceful 5427
 
2.8%
air/drone 2497
 
1.3%
strike 2497
 
1.3%
disrupted 1077
 
0.6%
weapons 1077
 
0.6%
Other values (40) 9349
 
4.8%

Most occurring characters

ValueCountFrequency (%)
l 306331
13.1%
i 228020
 
9.7%
e 226093
 
9.7%
a 201909
 
8.6%
t 186371
 
8.0%
r 157359
 
6.7%
s 150863
 
6.4%
/ 113164
 
4.8%
97452
 
4.2%
c 91014
 
3.9%
Other values (32) 580919
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2029967
86.8%
Other Punctuation 113164
 
4.8%
Uppercase Letter 98536
 
4.2%
Space Separator 97452
 
4.2%
Dash Punctuation 376
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 306331
15.1%
i 228020
11.2%
e 226093
11.1%
a 201909
9.9%
t 186371
9.2%
r 157359
7.8%
s 150863
7.4%
c 91014
 
4.5%
m 84883
 
4.2%
h 83504
 
4.1%
Other values (14) 313620
15.4%
Uppercase Letter
ValueCountFrequency (%)
S 54039
54.8%
A 32299
32.8%
P 5563
 
5.6%
D 1895
 
1.9%
E 836
 
0.8%
R 818
 
0.8%
I 818
 
0.8%
L 389
 
0.4%
G 379
 
0.4%
O 377
 
0.4%
Other values (5) 1123
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/ 113164
100.0%
Space Separator
ValueCountFrequency (%)
97452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2128503
91.0%
Common 210992
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 306331
14.4%
i 228020
10.7%
e 226093
10.6%
a 201909
9.5%
t 186371
8.8%
r 157359
 
7.4%
s 150863
 
7.1%
c 91014
 
4.3%
m 84883
 
4.0%
h 83504
 
3.9%
Other values (29) 412156
19.4%
Common
ValueCountFrequency (%)
/ 113164
53.6%
97452
46.2%
- 376
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2339495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 306331
13.1%
i 228020
 
9.7%
e 226093
 
9.7%
a 201909
 
8.6%
t 186371
 
8.0%
r 157359
 
6.7%
s 150863
 
6.4%
/ 113164
 
4.8%
97452
 
4.2%
c 91014
 
3.9%
Other values (32) 580919
24.8%

ACTOR1
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Military Forces of Russia (2000-)
32391 
NAF: United Armed Forces of Novorossiya
20543 
Military Forces of Ukraine (2019-)
20063 
Military Forces of Ukraine (2014-2019)
12230 
Protesters (Ukraine)
5533 
Other values (110)
5322 

Length

Max length148
Median length71
Mean length34.598832
Min length3

Characters and Unicode

Total characters3324325
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)< 0.1%

Sample

1st rowPolice Forces of Romania (2016-2019) Coast Guard
2nd rowMilitary Forces of Romania (2021-)
3rd rowProtesters (Romania)
4th rowMilitary Forces of Romania (2021-)
5th rowProtesters (Romania)

Common Values

ValueCountFrequency (%)
Military Forces of Russia (2000-) 32391
33.7%
NAF: United Armed Forces of Novorossiya 20543
21.4%
Military Forces of Ukraine (2019-) 20063
20.9%
Military Forces of Ukraine (2014-2019) 12230
 
12.7%
Protesters (Ukraine) 5533
 
5.8%
Military Forces of Russia (2000-) Air Force 2248
 
2.3%
Unidentified Armed Group (Ukraine) 1010
 
1.1%
Rioters (Ukraine) 418
 
0.4%
Civilians (Ukraine) 334
 
0.3%
Military Forces of Ukraine (2019-) Air Force 204
 
0.2%
Other values (105) 1108
 
1.2%

Length

2023-03-29T18:15:17.647665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 88484
18.2%
forces 88452
18.2%
military 67458
13.9%
ukraine 40607
8.3%
russia 34765
 
7.1%
2000 34737
 
7.1%
armed 21559
 
4.4%
2019 20589
 
4.2%
united 20545
 
4.2%
naf 20543
 
4.2%
Other values (130) 48612
10.0%

Most occurring characters

ValueCountFrequency (%)
390269
 
11.7%
i 260366
 
7.8%
r 257116
 
7.7%
o 249121
 
7.5%
s 211202
 
6.4%
e 189658
 
5.7%
a 164627
 
5.0%
0 149623
 
4.5%
F 111468
 
3.4%
t 101598
 
3.1%
Other values (50) 1239277
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2003437
60.3%
Space Separator 390269
 
11.7%
Uppercase Letter 371168
 
11.2%
Decimal Number 320608
 
9.6%
Open Punctuation 75231
 
2.3%
Close Punctuation 75231
 
2.3%
Dash Punctuation 67788
 
2.0%
Other Punctuation 20593
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 111468
30.0%
M 67661
18.2%
U 62359
16.8%
A 44562
 
12.0%
N 41166
 
11.1%
R 35252
 
9.5%
P 6101
 
1.6%
G 1218
 
0.3%
S 514
 
0.1%
C 483
 
0.1%
Other values (13) 384
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
i 260366
13.0%
r 257116
12.8%
o 249121
12.4%
s 211202
10.5%
e 189658
9.5%
a 164627
8.2%
t 101598
 
5.1%
c 91886
 
4.6%
f 89709
 
4.5%
y 88278
 
4.4%
Other values (12) 299876
15.0%
Decimal Number
ValueCountFrequency (%)
0 149623
46.7%
2 80155
25.0%
1 45417
 
14.2%
9 32995
 
10.3%
4 12408
 
3.9%
6 7
 
< 0.1%
7 2
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 20545
99.8%
' 47
 
0.2%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
390269
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75231
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75231
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2374605
71.4%
Common 949720
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 260366
11.0%
r 257116
 
10.8%
o 249121
 
10.5%
s 211202
 
8.9%
e 189658
 
8.0%
a 164627
 
6.9%
F 111468
 
4.7%
t 101598
 
4.3%
c 91886
 
3.9%
f 89709
 
3.8%
Other values (35) 647854
27.3%
Common
ValueCountFrequency (%)
390269
41.1%
0 149623
 
15.8%
2 80155
 
8.4%
( 75231
 
7.9%
) 75231
 
7.9%
- 67788
 
7.1%
1 45417
 
4.8%
9 32995
 
3.5%
: 20545
 
2.2%
4 12408
 
1.3%
Other values (5) 58
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3324325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
390269
 
11.7%
i 260366
 
7.8%
r 257116
 
7.7%
o 249121
 
7.5%
s 211202
 
6.4%
e 189658
 
5.7%
a 164627
 
5.0%
0 149623
 
4.5%
F 111468
 
3.4%
t 101598
 
3.1%
Other values (50) 1239277
37.3%

ASSOC_ACTOR_1
Categorical

HIGH CARDINALITY  MISSING 

Distinct691
Distinct (%)10.7%
Missing89594
Missing (%)93.2%
Memory size750.8 KiB
Military Forces of Russia (2000-)
1060 
National Corps Party
694 
Labor Group (Ukraine)
515 
Military Forces of Russia (2000-) Air Force
 
353
Refugees/IDPs (Ukraine)
 
301
Other values (686)
3565 

Length

Max length234
Median length177
Mean length30.876695
Min length3

Characters and Unicode

Total characters200328
Distinct characters64
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique476 ?
Unique (%)7.3%

Sample

1st rowGreenpeace
2nd rowGreenpeace
3rd rowSvoboda; Sokil
4th rowSvoboda; Sokil; Military Forces of Ukraine (2014-2019)
5th rowSvoboda; National Corps Party

Common Values

ValueCountFrequency (%)
Military Forces of Russia (2000-) 1060
 
1.1%
National Corps Party 694
 
0.7%
Labor Group (Ukraine) 515
 
0.5%
Military Forces of Russia (2000-) Air Force 353
 
0.4%
Refugees/IDPs (Ukraine) 301
 
0.3%
Donbass People's Militia 251
 
0.3%
Stop Corruption 237
 
0.2%
Luhansk People's Militia 223
 
0.2%
Traditions and Order 186
 
0.2%
Wagner Group 113
 
0.1%
Other values (681) 2555
 
2.7%
(Missing) 89594
93.2%

Length

2023-03-29T18:15:17.836065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 2362
 
8.5%
ukraine 2299
 
8.2%
forces 2182
 
7.8%
military 1962
 
7.0%
russia 1547
 
5.5%
2000 1522
 
5.5%
party 1375
 
4.9%
national 1226
 
4.4%
corps 1096
 
3.9%
group 1015
 
3.6%
Other values (264) 11310
40.5%

Most occurring characters

ValueCountFrequency (%)
21408
 
10.7%
r 16541
 
8.3%
a 15098
 
7.5%
i 14907
 
7.4%
o 14858
 
7.4%
e 10774
 
5.4%
s 10316
 
5.1%
t 8771
 
4.4%
n 6970
 
3.5%
l 5526
 
2.8%
Other values (54) 75159
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 131804
65.8%
Uppercase Letter 24840
 
12.4%
Space Separator 21408
 
10.7%
Decimal Number 9392
 
4.7%
Open Punctuation 3942
 
2.0%
Close Punctuation 3942
 
2.0%
Other Punctuation 2816
 
1.4%
Dash Punctuation 2148
 
1.1%
Math Symbol 36
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16541
12.5%
a 15098
11.5%
i 14907
11.3%
o 14858
11.3%
e 10774
8.2%
s 10316
7.8%
t 8771
 
6.7%
n 6970
 
5.3%
l 5526
 
4.2%
u 4130
 
3.1%
Other values (15) 23913
18.1%
Uppercase Letter
ValueCountFrequency (%)
F 3337
13.4%
M 2853
11.5%
P 2691
10.8%
U 2565
10.3%
R 2254
9.1%
C 1820
7.3%
N 1558
 
6.3%
S 1324
 
5.3%
G 1190
 
4.8%
L 1025
 
4.1%
Other values (14) 4223
17.0%
Decimal Number
ValueCountFrequency (%)
0 5358
57.0%
2 2312
24.6%
1 858
 
9.1%
9 595
 
6.3%
4 269
 
2.9%
Other Punctuation
ValueCountFrequency (%)
; 1671
59.3%
' 619
 
22.0%
/ 304
 
10.8%
: 221
 
7.8%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
21408
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3942
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3942
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2148
100.0%
Math Symbol
ValueCountFrequency (%)
+ 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 156644
78.2%
Common 43684
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16541
 
10.6%
a 15098
 
9.6%
i 14907
 
9.5%
o 14858
 
9.5%
e 10774
 
6.9%
s 10316
 
6.6%
t 8771
 
5.6%
n 6970
 
4.4%
l 5526
 
3.5%
u 4130
 
2.6%
Other values (39) 48753
31.1%
Common
ValueCountFrequency (%)
21408
49.0%
0 5358
 
12.3%
( 3942
 
9.0%
) 3942
 
9.0%
2 2312
 
5.3%
- 2148
 
4.9%
; 1671
 
3.8%
1 858
 
2.0%
' 619
 
1.4%
9 595
 
1.4%
Other values (5) 831
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21408
 
10.7%
r 16541
 
8.3%
a 15098
 
7.5%
i 14907
 
7.4%
o 14858
 
7.4%
e 10774
 
5.4%
s 10316
 
5.1%
t 8771
 
4.4%
n 6970
 
3.5%
l 5526
 
2.8%
Other values (54) 75159
37.5%

INTER1
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1179826
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:17.969409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.16869
Coefficient of variation (CV)0.76947631
Kurtosis-1.804966
Mean4.1179826
Median Absolute Deviation (MAD)1
Skewness0.29285778
Sum395664
Variance10.040596
MonotonicityNot monotonic
2023-03-29T18:15:18.076058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 34993
36.4%
1 33004
34.3%
2 20583
21.4%
6 5581
 
5.8%
3 1042
 
1.1%
5 420
 
0.4%
7 334
 
0.3%
4 125
 
0.1%
ValueCountFrequency (%)
1 33004
34.3%
2 20583
21.4%
3 1042
 
1.1%
4 125
 
0.1%
5 420
 
0.4%
6 5581
 
5.8%
7 334
 
0.3%
8 34993
36.4%
ValueCountFrequency (%)
8 34993
36.4%
7 334
 
0.3%
6 5581
 
5.8%
5 420
 
0.4%
4 125
 
0.1%
3 1042
 
1.1%
2 20583
21.4%
1 33004
34.3%

ACTOR2
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE  MISSING 

Distinct88
Distinct (%)0.2%
Missing44253
Missing (%)46.1%
Memory size750.8 KiB
NAF: United Armed Forces of Novorossiya
19387 
Military Forces of Ukraine (2019-)
14713 
Military Forces of Ukraine (2014-2019)
8045 
Civilians (Ukraine)
4542 
Military Forces of Russia (2000-)
2791 
Other values (83)
2351 

Length

Max length71
Median length69
Mean length35.536553
Min length12

Characters and Unicode

Total characters1841824
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.1%

Sample

1st rowCivilians (Turkey)
2nd rowUnidentified Military Forces
3rd rowUnidentified Armed Group (International)
4th rowMilitary Forces of Romania (2021-)
5th rowUnidentified Military Forces

Common Values

ValueCountFrequency (%)
NAF: United Armed Forces of Novorossiya 19387
20.2%
Military Forces of Ukraine (2019-) 14713
 
15.3%
Military Forces of Ukraine (2014-2019) 8045
 
8.4%
Civilians (Ukraine) 4542
 
4.7%
Military Forces of Russia (2000-) 2791
 
2.9%
Military Forces of Russia (2000-) Donetsk People's Militia 667
 
0.7%
Military Forces of Russia (2000-) Air Force 436
 
0.5%
Unidentified Armed Group (Ukraine) 269
 
0.3%
Military Forces of Ukraine (2019-) Air Force 138
 
0.1%
Police Forces of Ukraine (2019-) 124
 
0.1%
Other values (78) 717
 
0.7%
(Missing) 44253
46.1%

Length

2023-03-29T18:15:18.226555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 46724
17.5%
forces 46696
17.5%
ukraine 28289
10.6%
military 26941
10.1%
armed 19658
7.3%
united 19390
7.2%
naf 19387
7.2%
novorossiya 19387
7.2%
2019 15070
 
5.6%
2014-2019 8167
 
3.1%
Other values (85) 17807
 
6.7%

Most occurring characters

ValueCountFrequency (%)
215687
 
11.7%
o 154541
 
8.4%
i 143302
 
7.8%
r 143095
 
7.8%
e 118477
 
6.4%
s 99995
 
5.4%
a 84569
 
4.6%
F 66661
 
3.6%
n 53971
 
2.9%
t 48636
 
2.6%
Other values (47) 712890
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1139844
61.9%
Uppercase Letter 232268
 
12.6%
Space Separator 215687
 
11.7%
Decimal Number 141808
 
7.7%
Close Punctuation 32403
 
1.8%
Open Punctuation 32403
 
1.8%
Dash Punctuation 27297
 
1.5%
Other Punctuation 20114
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 154541
13.6%
i 143302
12.6%
r 143095
12.6%
e 118477
10.4%
s 99995
8.8%
a 84569
7.4%
n 53971
 
4.7%
t 48636
 
4.3%
c 47792
 
4.2%
f 47021
 
4.1%
Other values (16) 198445
17.4%
Uppercase Letter
ValueCountFrequency (%)
F 66661
28.7%
U 47970
20.7%
A 39624
17.1%
N 38830
16.7%
M 27669
11.9%
C 4660
 
2.0%
R 4216
 
1.8%
P 1164
 
0.5%
D 680
 
0.3%
G 389
 
0.2%
Other values (10) 405
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 43546
30.7%
2 35453
25.0%
1 31405
22.1%
9 23237
16.4%
4 8167
 
5.8%
Other Punctuation
ValueCountFrequency (%)
: 19387
96.4%
' 727
 
3.6%
Space Separator
ValueCountFrequency (%)
215687
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32403
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27297
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1372112
74.5%
Common 469712
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 154541
 
11.3%
i 143302
 
10.4%
r 143095
 
10.4%
e 118477
 
8.6%
s 99995
 
7.3%
a 84569
 
6.2%
F 66661
 
4.9%
n 53971
 
3.9%
t 48636
 
3.5%
U 47970
 
3.5%
Other values (36) 410895
29.9%
Common
ValueCountFrequency (%)
215687
45.9%
0 43546
 
9.3%
2 35453
 
7.5%
) 32403
 
6.9%
( 32403
 
6.9%
1 31405
 
6.7%
- 27297
 
5.8%
9 23237
 
4.9%
: 19387
 
4.1%
4 8167
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1841824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
215687
 
11.7%
o 154541
 
8.4%
i 143302
 
7.8%
r 143095
 
7.8%
e 118477
 
6.4%
s 99995
 
5.4%
a 84569
 
4.6%
F 66661
 
3.6%
n 53971
 
2.9%
t 48636
 
2.6%
Other values (47) 712890
38.7%

ASSOC_ACTOR_2
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct378
Distinct (%)2.6%
Missing81404
Missing (%)84.7%
Memory size750.8 KiB
Donbass People's Militia
9155 
Luhansk People's Militia
2825 
Donbass People's Militia; Civilians (Ukraine)
 
638
Civilians (Ukraine)
 
272
Luhansk People's Militia; Civilians (Ukraine)
 
161
Other values (373)
1627 

Length

Max length204
Median length24
Mean length26.599741
Min length3

Characters and Unicode

Total characters390431
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)1.6%

Sample

1st rowFishers (Turkey)
2nd rowRefugees/IDPs (Afghanistan); Civilians (Syria); Refugees/IDPs (Syria); Civilians (Iran); Refugees/IDPs (Iran)
3rd rowRefugees/IDPs (International)
4th rowRefugees/IDPs (International)
5th rowLuhansk People's Militia

Common Values

ValueCountFrequency (%)
Donbass People's Militia 9155
 
9.5%
Luhansk People's Militia 2825
 
2.9%
Donbass People's Militia; Civilians (Ukraine) 638
 
0.7%
Civilians (Ukraine) 272
 
0.3%
Luhansk People's Militia; Civilians (Ukraine) 161
 
0.2%
Government of Ukraine (2019-) 98
 
0.1%
Labor Group (Ukraine) 93
 
0.1%
Military Forces of Ukraine (2019-) Air Force 76
 
0.1%
Journalists (Ukraine) 73
 
0.1%
Farmers (Ukraine) 61
 
0.1%
Other values (368) 1226
 
1.3%
(Missing) 81404
84.7%

Length

2023-03-29T18:15:18.405622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
people's 12902
26.4%
militia 12882
26.4%
donbass 9864
20.2%
luhansk 3020
 
6.2%
ukraine 2446
 
5.0%
civilians 1145
 
2.3%
of 855
 
1.8%
forces 558
 
1.1%
2019 448
 
0.9%
military 376
 
0.8%
Other values (234) 4285
 
8.8%

Most occurring characters

ValueCountFrequency (%)
i 47232
12.1%
s 38781
 
9.9%
34103
 
8.7%
a 31608
 
8.1%
e 31120
 
8.0%
l 27910
 
7.1%
o 26632
 
6.8%
n 18409
 
4.7%
t 14841
 
3.8%
p 13435
 
3.4%
Other values (58) 106360
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 284934
73.0%
Uppercase Letter 47316
 
12.1%
Space Separator 34103
 
8.7%
Other Punctuation 14295
 
3.7%
Decimal Number 3568
 
0.9%
Open Punctuation 2674
 
0.7%
Close Punctuation 2674
 
0.7%
Dash Punctuation 840
 
0.2%
Math Symbol 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 47232
16.6%
s 38781
13.6%
a 31608
11.1%
e 31120
10.9%
l 27910
9.8%
o 26632
9.3%
n 18409
 
6.5%
t 14841
 
5.2%
p 13435
 
4.7%
b 10019
 
3.5%
Other values (15) 24947
8.8%
Uppercase Letter
ValueCountFrequency (%)
M 13298
28.1%
P 13241
28.0%
D 9917
21.0%
L 3214
 
6.8%
U 2521
 
5.3%
C 1377
 
2.9%
F 943
 
2.0%
G 614
 
1.3%
R 383
 
0.8%
S 309
 
0.7%
Other values (15) 1499
 
3.2%
Decimal Number
ValueCountFrequency (%)
0 1222
34.2%
2 887
24.9%
1 728
20.4%
9 585
16.4%
4 143
 
4.0%
8 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 12904
90.3%
; 1293
 
9.0%
: 85
 
0.6%
/ 12
 
0.1%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
34103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2674
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2674
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 840
100.0%
Math Symbol
ValueCountFrequency (%)
+ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332250
85.1%
Common 58181
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 47232
14.2%
s 38781
11.7%
a 31608
9.5%
e 31120
9.4%
l 27910
8.4%
o 26632
 
8.0%
n 18409
 
5.5%
t 14841
 
4.5%
p 13435
 
4.0%
M 13298
 
4.0%
Other values (40) 68984
20.8%
Common
ValueCountFrequency (%)
34103
58.6%
' 12904
 
22.2%
( 2674
 
4.6%
) 2674
 
4.6%
; 1293
 
2.2%
0 1222
 
2.1%
2 887
 
1.5%
- 840
 
1.4%
1 728
 
1.3%
9 585
 
1.0%
Other values (8) 271
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 47232
12.1%
s 38781
 
9.9%
34103
 
8.7%
a 31608
 
8.1%
e 31120
 
8.0%
l 27910
 
7.1%
o 26632
 
6.8%
n 18409
 
4.7%
t 14841
 
3.8%
p 13435
 
3.4%
Other values (58) 106360
27.2%

INTER2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3435295
Minimum0
Maximum8
Zeros44253
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:18.562878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1025779
Coefficient of variation (CV)1.5649659
Kurtosis3.9236426
Mean1.3435295
Median Absolute Deviation (MAD)1
Skewness2.1941353
Sum129089
Variance4.4208337
MonotonicityNot monotonic
2023-03-29T18:15:18.673359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 44253
46.1%
1 23238
24.2%
2 19398
20.2%
7 4642
 
4.8%
8 4100
 
4.3%
3 276
 
0.3%
5 109
 
0.1%
6 62
 
0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
0 44253
46.1%
1 23238
24.2%
2 19398
20.2%
3 276
 
0.3%
4 4
 
< 0.1%
5 109
 
0.1%
6 62
 
0.1%
7 4642
 
4.8%
8 4100
 
4.3%
ValueCountFrequency (%)
8 4100
 
4.3%
7 4642
 
4.8%
6 62
 
0.1%
5 109
 
0.1%
4 4
 
< 0.1%
3 276
 
0.3%
2 19398
20.2%
1 23238
24.2%
0 44253
46.1%

INTERACTION
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.572105
Minimum10
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:18.815092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median18
Q378
95-th percentile80
Maximum88
Range78
Interquartile range (IQR)66

Descriptive statistics

Standard deviation30.176587
Coefficient of variation (CV)0.84832166
Kurtosis-1.4611103
Mean35.572105
Median Absolute Deviation (MAD)6
Skewness0.66259346
Sum3417839
Variance910.62642
MonotonicityNot monotonic
2023-03-29T18:15:18.962601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
12 34488
35.9%
80 23643
24.6%
18 11696
 
12.2%
10 9284
 
9.7%
60 5365
 
5.6%
20 5349
 
5.6%
78 3557
 
3.7%
37 644
 
0.7%
70 334
 
0.3%
13 328
 
0.3%
Other values (27) 1394
 
1.5%
ValueCountFrequency (%)
10 9284
 
9.7%
11 20
 
< 0.1%
12 34488
35.9%
13 328
 
0.3%
14 6
 
< 0.1%
15 136
 
0.1%
16 57
 
0.1%
17 207
 
0.2%
18 11696
 
12.2%
20 5349
 
5.6%
ValueCountFrequency (%)
88 30
 
< 0.1%
80 23643
24.6%
78 3557
 
3.7%
70 334
 
0.3%
68 55
 
0.1%
66 62
 
0.1%
60 5365
 
5.6%
58 20
 
< 0.1%
57 98
 
0.1%
56 40
 
< 0.1%

CIVILIAN_TARGETING
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing91894
Missing (%)95.6%
Memory size750.8 KiB
Civilian targeting
4188 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters75384
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCivilian targeting
2nd rowCivilian targeting
3rd rowCivilian targeting
4th rowCivilian targeting
5th rowCivilian targeting

Common Values

ValueCountFrequency (%)
Civilian targeting 4188
 
4.4%
(Missing) 91894
95.6%

Length

2023-03-29T18:15:19.094455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:19.205158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
civilian 4188
50.0%
targeting 4188
50.0%

Most occurring characters

ValueCountFrequency (%)
i 16752
22.2%
a 8376
11.1%
n 8376
11.1%
t 8376
11.1%
g 8376
11.1%
C 4188
 
5.6%
v 4188
 
5.6%
l 4188
 
5.6%
4188
 
5.6%
r 4188
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67008
88.9%
Uppercase Letter 4188
 
5.6%
Space Separator 4188
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 16752
25.0%
a 8376
12.5%
n 8376
12.5%
t 8376
12.5%
g 8376
12.5%
v 4188
 
6.2%
l 4188
 
6.2%
r 4188
 
6.2%
e 4188
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
C 4188
100.0%
Space Separator
ValueCountFrequency (%)
4188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71196
94.4%
Common 4188
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 16752
23.5%
a 8376
11.8%
n 8376
11.8%
t 8376
11.8%
g 8376
11.8%
C 4188
 
5.9%
v 4188
 
5.9%
l 4188
 
5.9%
r 4188
 
5.9%
e 4188
 
5.9%
Common
ValueCountFrequency (%)
4188
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 16752
22.2%
a 8376
11.1%
n 8376
11.1%
t 8376
11.1%
g 8376
11.1%
C 4188
 
5.6%
v 4188
 
5.6%
l 4188
 
5.6%
4188
 
5.6%
r 4188
 
5.6%

ISO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
804
96067 
642
 
8
792
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters288246
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row642
2nd row642
3rd row642
4th row642
5th row642

Common Values

ValueCountFrequency (%)
804 96067
> 99.9%
642 8
 
< 0.1%
792 7
 
< 0.1%

Length

2023-03-29T18:15:19.301835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:19.409520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
804 96067
> 99.9%
642 8
 
< 0.1%
792 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 96075
33.3%
8 96067
33.3%
0 96067
33.3%
2 15
 
< 0.1%
6 8
 
< 0.1%
7 7
 
< 0.1%
9 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288246
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 96075
33.3%
8 96067
33.3%
0 96067
33.3%
2 15
 
< 0.1%
6 8
 
< 0.1%
7 7
 
< 0.1%
9 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 288246
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 96075
33.3%
8 96067
33.3%
0 96067
33.3%
2 15
 
< 0.1%
6 8
 
< 0.1%
7 7
 
< 0.1%
9 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 96075
33.3%
8 96067
33.3%
0 96067
33.3%
2 15
 
< 0.1%
6 8
 
< 0.1%
7 7
 
< 0.1%
9 7
 
< 0.1%

REGION
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Europe
96075 
Middle East
 
7

Length

Max length11
Median length6
Mean length6.0003643
Min length6

Characters and Unicode

Total characters576527
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEurope
2nd rowEurope
3rd rowEurope
4th rowEurope
5th rowEurope

Common Values

ValueCountFrequency (%)
Europe 96075
> 99.9%
Middle East 7
 
< 0.1%

Length

2023-03-29T18:15:19.504212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:19.624755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
europe 96075
> 99.9%
middle 7
 
< 0.1%
east 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 96082
16.7%
e 96082
16.7%
u 96075
16.7%
r 96075
16.7%
o 96075
16.7%
p 96075
16.7%
d 14
 
< 0.1%
M 7
 
< 0.1%
i 7
 
< 0.1%
l 7
 
< 0.1%
Other values (4) 28
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 480431
83.3%
Uppercase Letter 96089
 
16.7%
Space Separator 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 96082
20.0%
u 96075
20.0%
r 96075
20.0%
o 96075
20.0%
p 96075
20.0%
d 14
 
< 0.1%
i 7
 
< 0.1%
l 7
 
< 0.1%
a 7
 
< 0.1%
s 7
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
E 96082
> 99.9%
M 7
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 576520
> 99.9%
Common 7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 96082
16.7%
e 96082
16.7%
u 96075
16.7%
r 96075
16.7%
o 96075
16.7%
p 96075
16.7%
d 14
 
< 0.1%
M 7
 
< 0.1%
i 7
 
< 0.1%
l 7
 
< 0.1%
Other values (3) 21
 
< 0.1%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 96082
16.7%
e 96082
16.7%
u 96075
16.7%
r 96075
16.7%
o 96075
16.7%
p 96075
16.7%
d 14
 
< 0.1%
M 7
 
< 0.1%
i 7
 
< 0.1%
l 7
 
< 0.1%
Other values (4) 28
 
< 0.1%

COUNTRY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Ukraine
96067 
Romania
 
8
Turkey
 
7

Length

Max length7
Median length7
Mean length6.9999271
Min length6

Characters and Unicode

Total characters672567
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRomania
2nd rowRomania
3rd rowRomania
4th rowRomania
5th rowRomania

Common Values

ValueCountFrequency (%)
Ukraine 96067
> 99.9%
Romania 8
 
< 0.1%
Turkey 7
 
< 0.1%

Length

2023-03-29T18:15:19.721477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:19.840036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ukraine 96067
> 99.9%
romania 8
 
< 0.1%
turkey 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 96083
14.3%
i 96075
14.3%
n 96075
14.3%
k 96074
14.3%
r 96074
14.3%
e 96074
14.3%
U 96067
14.3%
R 8
 
< 0.1%
o 8
 
< 0.1%
m 8
 
< 0.1%
Other values (3) 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 576485
85.7%
Uppercase Letter 96082
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 96083
16.7%
i 96075
16.7%
n 96075
16.7%
k 96074
16.7%
r 96074
16.7%
e 96074
16.7%
o 8
 
< 0.1%
m 8
 
< 0.1%
u 7
 
< 0.1%
y 7
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
U 96067
> 99.9%
R 8
 
< 0.1%
T 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 672567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 96083
14.3%
i 96075
14.3%
n 96075
14.3%
k 96074
14.3%
r 96074
14.3%
e 96074
14.3%
U 96067
14.3%
R 8
 
< 0.1%
o 8
 
< 0.1%
m 8
 
< 0.1%
Other values (3) 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 96083
14.3%
i 96075
14.3%
n 96075
14.3%
k 96074
14.3%
r 96074
14.3%
e 96074
14.3%
U 96067
14.3%
R 8
 
< 0.1%
o 8
 
< 0.1%
m 8
 
< 0.1%
Other values (3) 21
 
< 0.1%

ADMIN1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct31
Distinct (%)< 0.1%
Missing17
Missing (%)< 0.1%
Memory size750.8 KiB
Donetsk
52800 
Luhansk
13716 
Kharkiv
7804 
Zaporizhia
 
4866
Kherson
 
4206
Other values (26)
12673 

Length

Max length15
Median length7
Mean length7.2538802
Min length4

Characters and Unicode

Total characters696844
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowConstanta
2nd rowConstanta
3rd rowConstanta
4th rowConstanta
5th rowConstanta

Common Values

ValueCountFrequency (%)
Donetsk 52800
55.0%
Luhansk 13716
 
14.3%
Kharkiv 7804
 
8.1%
Zaporizhia 4866
 
5.1%
Kherson 4206
 
4.4%
Kyiv City 2227
 
2.3%
Sumy 2116
 
2.2%
Mykolaiv 1942
 
2.0%
Dnipropetrovsk 1502
 
1.6%
Chernihiv 882
 
0.9%
Other values (21) 4004
 
4.2%

Length

2023-03-29T18:15:19.936719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
donetsk 52800
53.7%
luhansk 13716
 
14.0%
kharkiv 7804
 
7.9%
zaporizhia 4866
 
5.0%
kherson 4206
 
4.3%
kyiv 2983
 
3.0%
city 2227
 
2.3%
sumy 2116
 
2.2%
mykolaiv 1942
 
2.0%
dnipropetrovsk 1502
 
1.5%
Other values (21) 4130
 
4.2%

Most occurring characters

ValueCountFrequency (%)
k 78445
11.3%
n 74275
10.7%
s 73667
10.6%
o 67801
9.7%
e 61092
8.8%
t 57509
8.3%
D 54302
7.8%
a 35801
 
5.1%
h 32951
 
4.7%
i 30047
 
4.3%
Other values (27) 130954
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 596015
85.5%
Uppercase Letter 98447
 
14.1%
Space Separator 2227
 
0.3%
Dash Punctuation 155
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k 78445
13.2%
n 74275
12.5%
s 73667
12.4%
o 67801
11.4%
e 61092
10.3%
t 57509
9.6%
a 35801
6.0%
h 32951
5.5%
i 30047
 
5.0%
r 22184
 
3.7%
Other values (11) 62243
10.4%
Uppercase Letter
ValueCountFrequency (%)
D 54302
55.2%
K 15201
 
15.4%
L 14248
 
14.5%
Z 5195
 
5.3%
C 3714
 
3.8%
S 2117
 
2.2%
M 1942
 
2.0%
O 760
 
0.8%
V 234
 
0.2%
P 166
 
0.2%
Other values (4) 568
 
0.6%
Space Separator
ValueCountFrequency (%)
2227
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 694462
99.7%
Common 2382
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
k 78445
11.3%
n 74275
10.7%
s 73667
10.6%
o 67801
9.8%
e 61092
8.8%
t 57509
8.3%
D 54302
7.8%
a 35801
 
5.2%
h 32951
 
4.7%
i 30047
 
4.3%
Other values (25) 128572
18.5%
Common
ValueCountFrequency (%)
2227
93.5%
- 155
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 78445
11.3%
n 74275
10.7%
s 73667
10.6%
o 67801
9.7%
e 61092
8.8%
t 57509
8.3%
D 54302
7.8%
a 35801
 
5.1%
h 32951
 
4.7%
i 30047
 
4.3%
Other values (27) 130954
18.8%

ADMIN2
Categorical

Distinct150
Distinct (%)0.2%
Missing91
Missing (%)0.1%
Memory size750.8 KiB
Bakhmutskyi
10381 
Donetskyi
9865 
Pokrovskyi
8604 
Sievierodonetskyi
6970 
Mariupolskyi
6934 
Other values (145)
53237 

Length

Max length21
Median length19
Mean length10.885041
Min length4

Characters and Unicode

Total characters1044866
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowMihai Viteazu
2nd rowSinop
3rd rowSile
4th rowSariyer
5th rowDemirkoy

Common Values

ValueCountFrequency (%)
Bakhmutskyi 10381
 
10.8%
Donetskyi 9865
 
10.3%
Pokrovskyi 8604
 
9.0%
Sievierodonetskyi 6970
 
7.3%
Mariupolskyi 6934
 
7.2%
Horlivskyi 5514
 
5.7%
Kalmiuskyi 4907
 
5.1%
Volnovaskyi 4025
 
4.2%
Alchevskyi 3962
 
4.1%
Polohivskyi 3018
 
3.1%
Other values (140) 31811
33.1%

Length

2023-03-29T18:15:20.068487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bakhmutskyi 10381
 
10.8%
donetskyi 9865
 
10.3%
pokrovskyi 8604
 
9.0%
sievierodonetskyi 6970
 
7.3%
mariupolskyi 6934
 
7.2%
horlivskyi 5514
 
5.7%
kalmiuskyi 4907
 
5.1%
volnovaskyi 4025
 
4.2%
alchevskyi 3962
 
4.1%
polohivskyi 3018
 
3.1%
Other values (141) 31812
33.1%

Most occurring characters

ValueCountFrequency (%)
i 146752
14.0%
k 119753
11.5%
y 102917
 
9.8%
s 99404
 
9.5%
o 83011
 
7.9%
v 49266
 
4.7%
a 46479
 
4.4%
r 44557
 
4.3%
e 41702
 
4.0%
t 34634
 
3.3%
Other values (34) 276391
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 947781
90.7%
Uppercase Letter 96538
 
9.2%
Dash Punctuation 546
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 146752
15.5%
k 119753
12.6%
y 102917
10.9%
s 99404
10.5%
o 83011
8.8%
v 49266
 
5.2%
a 46479
 
4.9%
r 44557
 
4.7%
e 41702
 
4.4%
t 34634
 
3.7%
Other values (12) 179306
18.9%
Uppercase Letter
ValueCountFrequency (%)
K 17125
17.7%
B 14186
14.7%
P 11846
12.3%
S 11307
11.7%
D 10333
10.7%
M 8492
8.8%
H 5605
 
5.8%
V 5007
 
5.2%
A 3962
 
4.1%
C 2014
 
2.1%
Other values (10) 6661
 
6.9%
Dash Punctuation
ValueCountFrequency (%)
- 546
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1044319
99.9%
Common 547
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 146752
14.1%
k 119753
11.5%
y 102917
 
9.9%
s 99404
 
9.5%
o 83011
 
7.9%
v 49266
 
4.7%
a 46479
 
4.5%
r 44557
 
4.3%
e 41702
 
4.0%
t 34634
 
3.3%
Other values (32) 275844
26.4%
Common
ValueCountFrequency (%)
- 546
99.8%
1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 146752
14.0%
k 119753
11.5%
y 102917
 
9.8%
s 99404
 
9.5%
o 83011
 
7.9%
v 49266
 
4.7%
a 46479
 
4.4%
r 44557
 
4.3%
e 41702
 
4.0%
t 34634
 
3.3%
Other values (34) 276391
26.5%

ADMIN3
Categorical

HIGH CARDINALITY  MISSING 

Distinct776
Distinct (%)0.8%
Missing2402
Missing (%)2.5%
Memory size750.8 KiB
Sartanska
 
6553
Donetska
 
4905
Svitlodarska
 
4742
Yasynuvatska
 
4603
Horlivska
 
4407
Other values (771)
68470 

Length

Max length31
Median length21
Mean length10.366193
Min length5

Characters and Unicode

Total characters971105
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)0.2%

Sample

1st rowDebaltsivska
2nd rowSvitlodarska
3rd rowSartanska
4th rowSartanska
5th rowYasynuvatska

Common Values

ValueCountFrequency (%)
Sartanska 6553
 
6.8%
Donetska 4905
 
5.1%
Svitlodarska 4742
 
4.9%
Yasynuvatska 4603
 
4.8%
Horlivska 4407
 
4.6%
Ocheretynska 4088
 
4.3%
Hirska 3910
 
4.1%
Kadiivska 3370
 
3.5%
Novoazovska 2687
 
2.8%
Marinska 2372
 
2.5%
Other values (766) 52043
54.2%
(Missing) 2402
 
2.5%

Length

2023-03-29T18:15:20.226647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sartanska 6553
 
7.0%
donetska 4905
 
5.2%
svitlodarska 4742
 
5.1%
yasynuvatska 4603
 
4.9%
horlivska 4407
 
4.7%
ocheretynska 4088
 
4.4%
hirska 3910
 
4.2%
kadiivska 3370
 
3.6%
novoazovska 2687
 
2.9%
marinska 2372
 
2.5%
Other values (768) 52073
55.6%

Most occurring characters

ValueCountFrequency (%)
a 163786
16.9%
s 108344
11.2%
k 106979
11.0%
i 65530
 
6.7%
o 58764
 
6.1%
v 54891
 
5.7%
n 49909
 
5.1%
r 48780
 
5.0%
t 37450
 
3.9%
e 35843
 
3.7%
Other values (36) 240829
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 875473
90.2%
Uppercase Letter 94656
 
9.7%
Dash Punctuation 946
 
0.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 163786
18.7%
s 108344
12.4%
k 106979
12.2%
i 65530
7.5%
o 58764
 
6.7%
v 54891
 
6.3%
n 49909
 
5.7%
r 48780
 
5.6%
t 37450
 
4.3%
e 35843
 
4.1%
Other values (13) 145197
16.6%
Uppercase Letter
ValueCountFrequency (%)
S 18326
19.4%
H 11008
11.6%
D 8980
9.5%
K 8206
8.7%
O 7262
 
7.7%
M 6016
 
6.4%
V 4856
 
5.1%
Y 4816
 
5.1%
B 4641
 
4.9%
N 4351
 
4.6%
Other values (11) 16194
17.1%
Dash Punctuation
ValueCountFrequency (%)
- 946
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 970129
99.9%
Common 976
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 163786
16.9%
s 108344
11.2%
k 106979
11.0%
i 65530
 
6.8%
o 58764
 
6.1%
v 54891
 
5.7%
n 49909
 
5.1%
r 48780
 
5.0%
t 37450
 
3.9%
e 35843
 
3.7%
Other values (34) 239853
24.7%
Common
ValueCountFrequency (%)
- 946
96.9%
30
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 971105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 163786
16.9%
s 108344
11.2%
k 106979
11.0%
i 65530
 
6.7%
o 58764
 
6.1%
v 54891
 
5.7%
n 49909
 
5.1%
r 48780
 
5.0%
t 37450
 
3.9%
e 35843
 
3.7%
Other values (36) 240829
24.8%

LOCATION
Categorical

Distinct2470
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Vodyane
 
1483
Avdiivka
 
1167
Donetsk Filtration Station
 
1089
Marinka
 
1041
Zaitseve
 
901
Other values (2465)
90401 

Length

Max length33
Median length26
Mean length10.216908
Min length3

Characters and Unicode

Total characters981661
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique647 ?
Unique (%)0.7%

Sample

1st rowCoast of Constanta
2nd rowCoast of Constanta
3rd rowCoast of Constanta
4th rowCoast of Constanta
5th rowCoast of Constanta

Common Values

ValueCountFrequency (%)
Vodyane 1483
 
1.5%
Avdiivka 1167
 
1.2%
Donetsk Filtration Station 1089
 
1.1%
Marinka 1041
 
1.1%
Zaitseve 901
 
0.9%
Luhanske 896
 
0.9%
Mineralne 825
 
0.9%
Kominternove 815
 
0.8%
Krasnohorivka 768
 
0.8%
Pyshchevyk 764
 
0.8%
Other values (2460) 86333
89.9%

Length

2023-03-29T18:15:20.392475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
donetsk 4289
 
3.7%
3825
 
3.3%
kyiv 2227
 
1.9%
vodyane 1483
 
1.3%
avdiivka 1167
 
1.0%
station 1145
 
1.0%
filtration 1089
 
0.9%
marinka 1041
 
0.9%
balka 966
 
0.8%
shakhta 930
 
0.8%
Other values (2461) 97351
84.3%

Most occurring characters

ValueCountFrequency (%)
a 98516
 
10.0%
o 80078
 
8.2%
i 75523
 
7.7%
k 75413
 
7.7%
e 73371
 
7.5%
v 63043
 
6.4%
n 55010
 
5.6%
r 48666
 
5.0%
s 44352
 
4.5%
y 43635
 
4.4%
Other values (48) 324054
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 842636
85.8%
Uppercase Letter 111820
 
11.4%
Space Separator 19431
 
2.0%
Dash Punctuation 6037
 
0.6%
Decimal Number 1448
 
0.1%
Other Punctuation 243
 
< 0.1%
Open Punctuation 23
 
< 0.1%
Close Punctuation 23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 98516
11.7%
o 80078
9.5%
i 75523
9.0%
k 75413
8.9%
e 73371
 
8.7%
v 63043
 
7.5%
n 55010
 
6.5%
r 48666
 
5.8%
s 44352
 
5.3%
y 43635
 
5.2%
Other values (14) 185029
22.0%
Uppercase Letter
ValueCountFrequency (%)
K 15682
14.0%
S 13164
11.8%
P 8718
 
7.8%
N 8484
 
7.6%
D 8341
 
7.5%
M 8012
 
7.2%
V 7390
 
6.6%
B 6570
 
5.9%
Z 5567
 
5.0%
L 4752
 
4.2%
Other values (13) 25140
22.5%
Decimal Number
ValueCountFrequency (%)
5 579
40.0%
4 361
24.9%
7 243
16.8%
6 243
16.8%
1 21
 
1.5%
2 1
 
0.1%
Space Separator
ValueCountFrequency (%)
19431
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6037
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 243
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 954456
97.2%
Common 27205
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 98516
 
10.3%
o 80078
 
8.4%
i 75523
 
7.9%
k 75413
 
7.9%
e 73371
 
7.7%
v 63043
 
6.6%
n 55010
 
5.8%
r 48666
 
5.1%
s 44352
 
4.6%
y 43635
 
4.6%
Other values (37) 296849
31.1%
Common
ValueCountFrequency (%)
19431
71.4%
- 6037
 
22.2%
5 579
 
2.1%
4 361
 
1.3%
7 243
 
0.9%
/ 243
 
0.9%
6 243
 
0.9%
( 23
 
0.1%
) 23
 
0.1%
1 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 98516
 
10.0%
o 80078
 
8.2%
i 75523
 
7.7%
k 75413
 
7.7%
e 73371
 
7.5%
v 63043
 
6.4%
n 55010
 
5.6%
r 48666
 
5.0%
s 44352
 
4.5%
y 43635
 
4.4%
Other values (48) 324054
33.0%

LATITUDE
Real number (ℝ)

Distinct2316
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.370409
Minimum41.162
Maximum52.341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:20.692658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum41.162
5-th percentile46.921
Q147.716
median48.268
Q348.719
95-th percentile50.45
Maximum52.341
Range11.179
Interquartile range (IQR)1.003

Descriptive statistics

Standard deviation1.0842564
Coefficient of variation (CV)0.022415696
Kurtosis1.542988
Mean48.370409
Median Absolute Deviation (MAD)0.481
Skewness0.81903324
Sum4647525.7
Variance1.1756119
MonotonicityNot monotonic
2023-03-29T18:15:20.840119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.148 1267
 
1.3%
48.139 1217
 
1.3%
48.133 1089
 
1.1%
47.943 1043
 
1.1%
47.127 987
 
1.0%
48.431 920
 
1.0%
48.429 902
 
0.9%
48.1 826
 
0.9%
47.175 815
 
0.8%
50.427 809
 
0.8%
Other values (2306) 86207
89.7%
ValueCountFrequency (%)
41.162 1
 
< 0.1%
41.24 1
 
< 0.1%
41.253 3
< 0.1%
41.852 1
 
< 0.1%
42.039 1
 
< 0.1%
43.389 7
< 0.1%
43.517 5
< 0.1%
44.156 7
< 0.1%
44.407 1
 
< 0.1%
44.416 1
 
< 0.1%
ValueCountFrequency (%)
52.341 3
 
< 0.1%
52.338 4
 
< 0.1%
52.334 41
< 0.1%
52.328 32
< 0.1%
52.32 1
 
< 0.1%
52.317 5
 
< 0.1%
52.313 25
< 0.1%
52.31 3
 
< 0.1%
52.308 2
 
< 0.1%
52.306 13
 
< 0.1%

LONGITUDE
Real number (ℝ)

Distinct2596
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.711842
Minimum22.163
Maximum40.132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:20.985682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.163
5-th percentile30.734
Q136.489
median37.779
Q337.993
95-th percentile38.635
Maximum40.132
Range17.969
Interquartile range (IQR)1.504

Descriptive statistics

Standard deviation2.5746416
Coefficient of variation (CV)0.070131092
Kurtosis6.3442494
Mean36.711842
Median Absolute Deviation (MAD)0.381
Skewness-2.3712511
Sum3527347.2
Variance6.6287791
MonotonicityNot monotonic
2023-03-29T18:15:21.127205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.75 1708
 
1.8%
37.788 1400
 
1.5%
37.808 1089
 
1.1%
37.792 1055
 
1.1%
37.825 1054
 
1.1%
37.505 1041
 
1.1%
38.009 901
 
0.9%
37.86 900
 
0.9%
37.809 858
 
0.9%
37.571 835
 
0.9%
Other values (2586) 85241
88.7%
ValueCountFrequency (%)
22.163 3
 
< 0.1%
22.206 3
 
< 0.1%
22.246 1
 
< 0.1%
22.3 60
0.1%
22.389 1
 
< 0.1%
22.393 1
 
< 0.1%
22.443 4
 
< 0.1%
22.46 2
 
< 0.1%
22.594 1
 
< 0.1%
22.596 1
 
< 0.1%
ValueCountFrequency (%)
40.132 2
 
< 0.1%
39.796 1
 
< 0.1%
39.747 5
< 0.1%
39.739 2
 
< 0.1%
39.697 2
 
< 0.1%
39.689 1
 
< 0.1%
39.674 1
 
< 0.1%
39.67 4
< 0.1%
39.668 1
 
< 0.1%
39.667 5
< 0.1%

GEO_PRECISION
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
2
69328 
1
25445 
3
 
1309

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96082
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

Length

2023-03-29T18:15:21.256726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T18:15:21.377849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

Most occurring characters

ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96082
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 96082
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 69328
72.2%
1 25445
 
26.5%
3 1309
 
1.4%

SOURCE
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct4819
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
OSCE SMM-Ukraine
23075 
Ministry of Defence of Ukraine
21204 
DPR Armed Forces Press Service
8906 
24 Channel
3741 
Suspilne Media
 
3238
Other values (4814)
35918 

Length

Max length288
Median length214
Mean length28.496971
Min length2

Characters and Unicode

Total characters2738046
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3635 ?
Unique (%)3.8%

Sample

1st rowDeschide; Hurriyet Daily; News.ro; CNN; TRT Haber
2nd rowAdevarul; G4media
3rd rowNews.ro
4th rowDigi24
5th rowNews.ro

Common Values

ValueCountFrequency (%)
OSCE SMM-Ukraine 23075
24.0%
Ministry of Defence of Ukraine 21204
22.1%
DPR Armed Forces Press Service 8906
 
9.3%
24 Channel 3741
 
3.9%
Suspilne Media 3238
 
3.4%
Institute for the Study of War 2226
 
2.3%
LPR People's Militia Press Service 2174
 
2.3%
JFO HQ press centre 2159
 
2.2%
Ministry of Defence of Ukraine; JFO HQ press centre 1998
 
2.1%
JFO HQ press centre; Ministry of Defence of Ukraine 1956
 
2.0%
Other values (4809) 25405
26.4%

Length

2023-03-29T18:15:21.533026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 68007
16.0%
ukraine 36089
 
8.5%
ministry 32355
 
7.6%
defence 32352
 
7.6%
osce 28159
 
6.6%
smm-ukraine 28159
 
6.6%
press 24547
 
5.8%
service 14125
 
3.3%
centre 11780
 
2.8%
forces 10637
 
2.5%
Other values (439) 139168
32.7%

Most occurring characters

ValueCountFrequency (%)
329296
 
12.0%
e 316704
 
11.6%
r 199924
 
7.3%
i 185867
 
6.8%
n 184795
 
6.7%
s 116640
 
4.3%
o 114675
 
4.2%
a 110536
 
4.0%
f 108453
 
4.0%
M 98089
 
3.6%
Other values (59) 973067
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1760513
64.3%
Uppercase Letter 563224
 
20.6%
Space Separator 329296
 
12.0%
Other Punctuation 38826
 
1.4%
Dash Punctuation 29394
 
1.1%
Decimal Number 16787
 
0.6%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 98089
17.4%
S 84035
14.9%
U 70218
12.5%
D 44739
7.9%
O 39376
7.0%
C 38271
 
6.8%
P 33361
 
5.9%
E 28447
 
5.1%
F 22251
 
4.0%
R 18111
 
3.2%
Other values (16) 86326
15.3%
Lowercase Letter
ValueCountFrequency (%)
e 316704
18.0%
r 199924
11.4%
i 185867
10.6%
n 184795
10.5%
s 116640
 
6.6%
o 114675
 
6.5%
a 110536
 
6.3%
f 108453
 
6.2%
t 74775
 
4.2%
k 71863
 
4.1%
Other values (15) 276281
15.7%
Decimal Number
ValueCountFrequency (%)
2 6285
37.4%
4 6046
36.0%
1 1446
 
8.6%
0 1421
 
8.5%
6 1190
 
7.1%
7 281
 
1.7%
9 58
 
0.3%
5 50
 
0.3%
3 10
 
0.1%
Other Punctuation
ValueCountFrequency (%)
; 32967
84.9%
' 3318
 
8.5%
. 2094
 
5.4%
: 256
 
0.7%
/ 191
 
0.5%
Space Separator
ValueCountFrequency (%)
329296
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2323737
84.9%
Common 414309
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 316704
 
13.6%
r 199924
 
8.6%
i 185867
 
8.0%
n 184795
 
8.0%
s 116640
 
5.0%
o 114675
 
4.9%
a 110536
 
4.8%
f 108453
 
4.7%
M 98089
 
4.2%
S 84035
 
3.6%
Other values (41) 804019
34.6%
Common
ValueCountFrequency (%)
329296
79.5%
; 32967
 
8.0%
- 29394
 
7.1%
2 6285
 
1.5%
4 6046
 
1.5%
' 3318
 
0.8%
. 2094
 
0.5%
1 1446
 
0.3%
0 1421
 
0.3%
6 1190
 
0.3%
Other values (8) 852
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2738046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
329296
 
12.0%
e 316704
 
11.6%
r 199924
 
7.3%
i 185867
 
6.8%
n 184795
 
6.7%
s 116640
 
4.3%
o 114675
 
4.2%
a 110536
 
4.0%
f 108453
 
4.0%
M 98089
 
3.6%
Other values (59) 973067
35.5%

SOURCE_SCALE
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Other
70390 
National
12983 
Other-National
 
4671
Subnational
 
2600
Local partner-New media
 
1851
Other values (13)
 
3587

Length

Max length25
Median length5
Mean length6.7987552
Min length5

Characters and Unicode

Total characters653238
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNational-International
2nd rowNational
3rd rowNational
4th rowNational
5th rowNational

Common Values

ValueCountFrequency (%)
Other 70390
73.3%
National 12983
 
13.5%
Other-National 4671
 
4.9%
Subnational 2600
 
2.7%
Local partner-New media 1851
 
1.9%
Other-Subnational 1149
 
1.2%
International 519
 
0.5%
New media 407
 
0.4%
Subnational-International 305
 
0.3%
Other-International 302
 
0.3%
Other values (8) 905
 
0.9%

Length

2023-03-29T18:15:21.703477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other 70390
70.0%
national 12983
 
12.9%
other-national 4671
 
4.6%
subnational 2600
 
2.6%
media 2411
 
2.4%
local 1857
 
1.8%
partner-new 1851
 
1.8%
other-subnational 1149
 
1.1%
new 630
 
0.6%
international 519
 
0.5%
Other values (10) 1512
 
1.5%

Most occurring characters

ValueCountFrequency (%)
t 104035
15.9%
e 85203
13.0%
r 81791
12.5%
O 76671
11.7%
h 76671
11.7%
a 54551
8.4%
n 33107
 
5.1%
i 26736
 
4.1%
o 25959
 
4.0%
l 25959
 
4.0%
Other values (15) 62555
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534301
81.8%
Uppercase Letter 105264
 
16.1%
Dash Punctuation 9182
 
1.4%
Space Separator 4491
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 104035
19.5%
e 85203
15.9%
r 81791
15.3%
h 76671
14.3%
a 54551
10.2%
n 33107
 
6.2%
i 26736
 
5.0%
o 25959
 
4.9%
l 25959
 
4.9%
b 4336
 
0.8%
Other values (7) 15953
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
O 76671
72.8%
N 20993
 
19.9%
S 4336
 
4.1%
L 1857
 
1.8%
I 1406
 
1.3%
R 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 9182
100.0%
Space Separator
ValueCountFrequency (%)
4491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 639565
97.9%
Common 13673
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 104035
16.3%
e 85203
13.3%
r 81791
12.8%
O 76671
12.0%
h 76671
12.0%
a 54551
8.5%
n 33107
 
5.2%
i 26736
 
4.2%
o 25959
 
4.1%
l 25959
 
4.1%
Other values (13) 48882
7.6%
Common
ValueCountFrequency (%)
- 9182
67.2%
4491
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 653238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 104035
15.9%
e 85203
13.0%
r 81791
12.5%
O 76671
11.7%
h 76671
11.7%
a 54551
8.4%
n 33107
 
5.1%
i 26736
 
4.1%
o 25959
 
4.0%
l 25959
 
4.0%
Other values (15) 62555
9.6%

NOTES
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct95577
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size750.8 KiB
Between 1 and 20 September, several dozen Donbass veterans, miners and local activists were blocking day and night a railway station in Sosnivka, Lviv, in order to prevent Russian imported coal from being delivered to a local factory.
 
20
On 28 May 2018, NAF rebel forces employed grenade launchers of various types and small arms against positions of Military forces of Ukraine near Verknyotoretske, Vodyane, Hnutove, Krasnohorivka, Marinka, Opytne, Pavlopil, Pisky, Shyrokyne and Butivka mine. During the day six Ukrainian Government soldiers were wounded and, according to intelligence reports, three NAF rebels were killed and five wounded at unspecified locations.[3 fatalities split among 18 events].
 
9
On 8 May 2018, NAF rebel forces used heavy machine guns, grenade launchers of various types and small arms to fire upon Military Forces of Ukraine positions near Pyshchevyk, Shyrokyne, Vodyane, Verknyotoretske, Hnutove, Krasnohorivka, Opytne, Pisky, Marinka, Pavlopil and Kamyanka. During the day four Ukrainian Government soldiers were wounded and, according to intelligence reports, one NAF rebel was killed and five wounded at unspecified locations. [1 fatality split among 21 events].
 
9
On 27 May 2018, NAF rebel forces employed grenade launchers of various types and infantry fighting vehicles against positions of Military forces of Ukraine near Vodyane, Marinka, Shyrokyne, Nevelske, Krasnohorivka, Talakivka, Pisky, Verknyotoretske and Butivka mine. During the day two Ukrainian Government soldiers were wounded and, according to intelligence reports, two NAF rebels were wounded at unspecified locations.
 
9
Displacement: On 8 April 2022, 3544 people were evacuated from Polohy, Vasylivka, Berdiansk, Tokmak, Melitopol, Enerhodar, Orikhiv, Huliaipole, Zaporizhia.
 
8
Other values (95572)
96027 

Length

Max length931
Median length597
Mean length155.81735
Min length62

Characters and Unicode

Total characters14971243
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95346 ?
Unique (%)99.2%

Sample

1st rowOn 20 May 2019, the Coast Guard of Romania fired at a Turkish fishing boat allegedly fishing in Romania's Exclusive Economic Zone (EEZ), 52 sea miles east of Costinesti [coded to Coast of Constanta]. Three Turkish fishers were reported to wounded.
2nd rowDefusal: On 28 March 2022, Romanian minesweepers conducted a controlled detonation of a sea mine 70km away from the Midia port off the Coast of Constanta. The mine was most likely strayed from a location near Ukraine.
3rd rowOn 28 July 2022, Greenpeace activists protested in front of an oil tanker off the Coast of Constanta, demanding a stop to the dependence on fossil fuels due to their effect on the climate.
4th rowDefusal: On 31 July 2022, Romanian Naval Forces defused and destroyed a YAM sea mine off the Coast of Constanta. The mine likely came from the Northern Black Sea region from the Ukrainian coast.
5th rowOn 4 August 2022, Greenpeace activists protested in front of an oil tanker off the Coast of Constanta, demanding a stop to the dependence on fossil fuels due to their effect on the climate.

Common Values

ValueCountFrequency (%)
Between 1 and 20 September, several dozen Donbass veterans, miners and local activists were blocking day and night a railway station in Sosnivka, Lviv, in order to prevent Russian imported coal from being delivered to a local factory. 20
 
< 0.1%
On 28 May 2018, NAF rebel forces employed grenade launchers of various types and small arms against positions of Military forces of Ukraine near Verknyotoretske, Vodyane, Hnutove, Krasnohorivka, Marinka, Opytne, Pavlopil, Pisky, Shyrokyne and Butivka mine. During the day six Ukrainian Government soldiers were wounded and, according to intelligence reports, three NAF rebels were killed and five wounded at unspecified locations.[3 fatalities split among 18 events]. 9
 
< 0.1%
On 8 May 2018, NAF rebel forces used heavy machine guns, grenade launchers of various types and small arms to fire upon Military Forces of Ukraine positions near Pyshchevyk, Shyrokyne, Vodyane, Verknyotoretske, Hnutove, Krasnohorivka, Opytne, Pisky, Marinka, Pavlopil and Kamyanka. During the day four Ukrainian Government soldiers were wounded and, according to intelligence reports, one NAF rebel was killed and five wounded at unspecified locations. [1 fatality split among 21 events]. 9
 
< 0.1%
On 27 May 2018, NAF rebel forces employed grenade launchers of various types and infantry fighting vehicles against positions of Military forces of Ukraine near Vodyane, Marinka, Shyrokyne, Nevelske, Krasnohorivka, Talakivka, Pisky, Verknyotoretske and Butivka mine. During the day two Ukrainian Government soldiers were wounded and, according to intelligence reports, two NAF rebels were wounded at unspecified locations. 9
 
< 0.1%
Displacement: On 8 April 2022, 3544 people were evacuated from Polohy, Vasylivka, Berdiansk, Tokmak, Melitopol, Enerhodar, Orikhiv, Huliaipole, Zaporizhia. 8
 
< 0.1%
On 24 February 2019, activists held protests in ten Ukrainian cities, including Kiev, Zhytomyr, Lviv, Kharkiv, Zaporizhia, Ternopil, Dnipro and Odessa, against the use of animal fur and demanding that authorities adopt a law prohibiting fur farms. 8
 
< 0.1%
On 24 May 2018, NAF rebel forces employed 120mm mortars, infantry fighting vehicles, grenade launchers and small arms to attack Military Forces of Ukraine positions near Vodyane, Berezove, Hnutove, Marinka, Pavlopil, Pisky, Troitske, Chermalyk, Shyrokyne and Butivka mine. During the day two Ukrainian Government soldiers were wounded at unspecified locations. 8
 
< 0.1%
On 26 May 2018, NAF rebel forces employed infantry fighting vehicles against positions of Military forces of Ukraine near Pavlopil, Shyrokyne, Marinka, Vodyane, Kamyanka, Opytne, Hnutove and Pisky. During the day two Ukrainian Government soldiers were wounded and, according to intelligence reports, two NAF rebels were wounded at unspecified locations. 8
 
< 0.1%
On 9 May 2018, Military Forces of Ukraine targeted with unknown weapons Dokuchayevsk and the outlying Petrovskoye village, the Yasinovataya area and the southern villages of Novolaspa, Kominternovo, Leninskoye, Oktyabr and Sosnovskoye. 8
 
< 0.1%
On 6 May 2018, Military Forces of Ukraine targeted with mortars, grenade launchers and small arms northern suburbs of Donetsk, Yasinovataya, Krutaya Balka, Vasiliyevka, Mikhailovo, Dolomitnoye, Dokuchayevsk and Styla. 8
 
< 0.1%
Other values (95567) 95987
99.9%

Length

2023-03-29T18:15:21.873955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 116687
 
5.1%
on 99144
 
4.3%
of 90183
 
3.9%
forces 76516
 
3.3%
near 62920
 
2.7%
unknown 53443
 
2.3%
a 42830
 
1.9%
and 42056
 
1.8%
russian 41737
 
1.8%
in 39184
 
1.7%
Other values (13753) 1634995
71.1%

Most occurring characters

ValueCountFrequency (%)
2203982
14.7%
e 1225581
 
8.2%
n 1090749
 
7.3%
a 981227
 
6.6%
s 854758
 
5.7%
o 845120
 
5.6%
i 837128
 
5.6%
r 761679
 
5.1%
t 670374
 
4.5%
l 492435
 
3.3%
Other values (73) 5008210
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10767422
71.9%
Space Separator 2203982
 
14.7%
Uppercase Letter 888100
 
5.9%
Decimal Number 670000
 
4.5%
Other Punctuation 409222
 
2.7%
Dash Punctuation 14377
 
0.1%
Close Punctuation 9065
 
0.1%
Open Punctuation 9065
 
0.1%
Math Symbol 8
 
< 0.1%
Currency Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1225581
11.4%
n 1090749
10.1%
a 981227
 
9.1%
s 854758
 
7.9%
o 845120
 
7.8%
i 837128
 
7.8%
r 761679
 
7.1%
t 670374
 
6.2%
l 492435
 
4.6%
u 346868
 
3.2%
Other values (16) 2661503
24.7%
Uppercase Letter
ValueCountFrequency (%)
O 139239
15.7%
M 104238
11.7%
S 86239
9.7%
C 71569
 
8.1%
D 69558
 
7.8%
R 55163
 
6.2%
U 43579
 
4.9%
F 43412
 
4.9%
A 38350
 
4.3%
N 36125
 
4.1%
Other values (16) 200628
22.6%
Other Punctuation
ValueCountFrequency (%)
, 207168
50.6%
. 162570
39.7%
/ 24564
 
6.0%
' 12968
 
3.2%
: 1797
 
0.4%
; 94
 
< 0.1%
! 22
 
< 0.1%
% 21
 
< 0.1%
# 10
 
< 0.1%
? 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 266356
39.8%
0 131290
19.6%
1 114977
17.2%
8 34760
 
5.2%
3 32210
 
4.8%
9 28348
 
4.2%
5 18022
 
2.7%
4 16411
 
2.4%
6 14026
 
2.1%
7 13600
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 4756
52.5%
] 4309
47.5%
Open Punctuation
ValueCountFrequency (%)
( 4755
52.5%
[ 4310
47.5%
Math Symbol
ValueCountFrequency (%)
+ 6
75.0%
= 2
 
25.0%
Space Separator
ValueCountFrequency (%)
2203982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14377
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11655522
77.9%
Common 3315721
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1225581
 
10.5%
n 1090749
 
9.4%
a 981227
 
8.4%
s 854758
 
7.3%
o 845120
 
7.3%
i 837128
 
7.2%
r 761679
 
6.5%
t 670374
 
5.8%
l 492435
 
4.2%
u 346868
 
3.0%
Other values (42) 3549603
30.5%
Common
ValueCountFrequency (%)
2203982
66.5%
2 266356
 
8.0%
, 207168
 
6.2%
. 162570
 
4.9%
0 131290
 
4.0%
1 114977
 
3.5%
8 34760
 
1.0%
3 32210
 
1.0%
9 28348
 
0.9%
/ 24564
 
0.7%
Other values (21) 109496
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14971243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2203982
14.7%
e 1225581
 
8.2%
n 1090749
 
7.3%
a 981227
 
6.6%
s 854758
 
5.7%
o 845120
 
5.6%
i 837128
 
5.6%
r 761679
 
5.1%
t 670374
 
4.5%
l 492435
 
3.3%
Other values (73) 5008210
33.5%

FATALITIES
Real number (ℝ)

SKEWED  ZEROS 

Distinct106
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44319435
Minimum0
Maximum600
Zeros91308
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:22.052114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum600
Range600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.3160898
Coefficient of variation (CV)14.251287
Kurtosis2807.4677
Mean0.44319435
Median Absolute Deviation (MAD)0
Skewness43.394402
Sum42583
Variance39.892991
MonotonicityNot monotonic
2023-03-29T18:15:22.224886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91308
95.0%
1 2441
 
2.5%
2 560
 
0.6%
10 364
 
0.4%
3 328
 
0.3%
4 109
 
0.1%
5 85
 
0.1%
6 67
 
0.1%
30 51
 
0.1%
7 48
 
< 0.1%
Other values (96) 721
 
0.8%
ValueCountFrequency (%)
0 91308
95.0%
1 2441
 
2.5%
2 560
 
0.6%
3 328
 
0.3%
4 109
 
0.1%
5 85
 
0.1%
6 67
 
0.1%
7 48
 
< 0.1%
8 35
 
< 0.1%
9 25
 
< 0.1%
ValueCountFrequency (%)
600 1
 
< 0.1%
500 2
 
< 0.1%
485 1
 
< 0.1%
400 2
 
< 0.1%
300 4
< 0.1%
250 2
 
< 0.1%
221 1
 
< 0.1%
220 1
 
< 0.1%
200 6
< 0.1%
180 2
 
< 0.1%

TAGS
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct358
Distinct (%)6.0%
Missing90144
Missing (%)93.8%
Memory size750.8 KiB
crowd size=no report
3419 
crowd size=several dozen
 
240
crowd size=about 100
 
213
crowd size=about 50
 
147
crowd size=about 200
 
107
Other values (353)
1812 

Length

Max length120
Median length20
Mean length20.494611
Min length12

Characters and Unicode

Total characters121697
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)3.2%

Sample

1st rowcrowd size=no report
2nd rowcrowd size=no report
3rd rowcrowd size=about 200
4th rowcrowd size=more than 100
5th rowcrowd size=more than 200

Common Values

ValueCountFrequency (%)
crowd size=no report 3419
 
3.6%
crowd size=several dozen 240
 
0.2%
crowd size=about 100 213
 
0.2%
crowd size=about 50 147
 
0.2%
crowd size=about 200 107
 
0.1%
crowd size=about 30 98
 
0.1%
crowd size=about 20 87
 
0.1%
crowd size=several hundred 83
 
0.1%
crowd size=hundreds 72
 
0.1%
crowd size=dozens 64
 
0.1%
Other values (348) 1408
 
1.5%
(Missing) 90144
93.8%

Length

2023-03-29T18:15:22.413877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crowd 5907
32.7%
size=no 3422
19.0%
report 3421
18.9%
size=about 1360
 
7.5%
size=several 364
 
2.0%
dozen 363
 
2.0%
100 286
 
1.6%
50 183
 
1.0%
200 147
 
0.8%
than 142
 
0.8%
Other values (246) 2460
13.6%

Most occurring characters

ValueCountFrequency (%)
o 15026
12.3%
r 13703
11.3%
12117
 
10.0%
e 11267
 
9.3%
d 6883
 
5.7%
s 6700
 
5.5%
z 6337
 
5.2%
w 6005
 
4.9%
i 6001
 
4.9%
c 5943
 
4.9%
Other values (31) 31715
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98470
80.9%
Space Separator 12117
 
10.0%
Math Symbol 5907
 
4.9%
Decimal Number 4985
 
4.1%
Dash Punctuation 149
 
0.1%
Other Punctuation 69
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 15026
15.3%
r 13703
13.9%
e 11267
11.4%
d 6883
7.0%
s 6700
6.8%
z 6337
6.4%
w 6005
 
6.1%
i 6001
 
6.1%
c 5943
 
6.0%
t 5421
 
5.5%
Other values (13) 15184
15.4%
Decimal Number
ValueCountFrequency (%)
0 2755
55.3%
1 643
 
12.9%
5 535
 
10.7%
2 411
 
8.2%
3 300
 
6.0%
4 142
 
2.8%
7 76
 
1.5%
6 73
 
1.5%
8 37
 
0.7%
9 13
 
0.3%
Other Punctuation
ValueCountFrequency (%)
: 37
53.6%
/ 18
26.1%
; 6
 
8.7%
, 6
 
8.7%
. 2
 
2.9%
Space Separator
ValueCountFrequency (%)
12117
100.0%
Math Symbol
ValueCountFrequency (%)
= 5907
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98470
80.9%
Common 23227
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 15026
15.3%
r 13703
13.9%
e 11267
11.4%
d 6883
7.0%
s 6700
6.8%
z 6337
6.4%
w 6005
 
6.1%
i 6001
 
6.1%
c 5943
 
6.0%
t 5421
 
5.5%
Other values (13) 15184
15.4%
Common
ValueCountFrequency (%)
12117
52.2%
= 5907
25.4%
0 2755
 
11.9%
1 643
 
2.8%
5 535
 
2.3%
2 411
 
1.8%
3 300
 
1.3%
- 149
 
0.6%
4 142
 
0.6%
7 76
 
0.3%
Other values (8) 192
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 15026
12.3%
r 13703
11.3%
12117
 
10.0%
e 11267
 
9.3%
d 6883
 
5.7%
s 6700
 
5.5%
z 6337
 
5.2%
w 6005
 
4.9%
i 6001
 
4.9%
c 5943
 
4.9%
Other values (31) 31715
26.1%

TIMESTAMP
Real number (ℝ)

Distinct8489
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6478314 × 109
Minimum1.5711644 × 109
Maximum1.6794373 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.8 KiB
2023-03-29T18:15:22.647347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5711644 × 109
5-th percentile1.6185016 × 109
Q11.6189499 × 109
median1.6498755 × 109
Q31.6643009 × 109
95-th percentile1.6770015 × 109
Maximum1.6794373 × 109
Range1.0827288 × 108
Interquartile range (IQR)45351025

Descriptive statistics

Standard deviation20372761
Coefficient of variation (CV)0.012363377
Kurtosis-1.2179228
Mean1.6478314 × 109
Median Absolute Deviation (MAD)16312729
Skewness-0.30228492
Sum1.5832693 × 1014
Variance4.150494 × 1014
MonotonicityNot monotonic
2023-03-29T18:15:22.873004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1658857905 495
 
0.5%
1658249348 472
 
0.5%
1660674709 437
 
0.5%
1659462993 425
 
0.4%
1660055881 419
 
0.4%
1675798464 408
 
0.4%
1656435887 402
 
0.4%
1675191967 394
 
0.4%
1664300891 393
 
0.4%
1673376406 387
 
0.4%
Other values (8479) 91850
95.6%
ValueCountFrequency (%)
1571164407 6
< 0.1%
1572403627 1
 
< 0.1%
1572403774 1
 
< 0.1%
1580849930 1
 
< 0.1%
1606149144 1
 
< 0.1%
1618436427 1
 
< 0.1%
1618436428 3
< 0.1%
1618436429 5
< 0.1%
1618436430 5
< 0.1%
1618436431 2
 
< 0.1%
ValueCountFrequency (%)
1679437290 13
 
< 0.1%
1679428447 1
 
< 0.1%
1679428446 9
 
< 0.1%
1679428444 2
 
< 0.1%
1679425924 133
0.1%
1679425923 284
0.3%
1679425922 302
0.3%
1679425921 288
0.3%
1679425920 16
 
< 0.1%
1678830929 15
 
< 0.1%

Interactions

2023-03-29T18:15:12.255636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:04.623596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.801386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.831139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.992558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.032569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.047236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.154293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.438164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:04.763224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.925015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.945463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.117941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.147727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.162796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.277489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.584425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:04.891052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.046738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.055096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.254602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.271379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.291117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.524285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.729022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.041542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.205628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.172025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.384104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.401775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.423872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.656841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.860691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.179371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.339801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.470921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.505734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.530104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.585330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.777441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.982479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.320936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.467373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.591489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.622478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.649242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.722247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.889067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:13.112135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.479522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.591824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.705481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.742785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.783873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:10.873228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.010658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:13.233731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:05.635738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:06.708205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:07.845317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:08.883276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:09.912440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:11.012697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-29T18:15:12.128827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-29T18:15:23.065614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YEARINTER1INTER2INTERACTIONLATITUDELONGITUDEFATALITIESTIMESTAMPTIME_PRECISIONDISORDER_TYPEEVENT_TYPESUB_EVENT_TYPEACTOR2ISOREGIONCOUNTRYADMIN1GEO_PRECISIONSOURCE_SCALE
YEAR1.0000.576-0.1200.6060.113-0.3140.0910.7600.0430.1400.1960.2200.4020.0000.0000.0000.2460.2180.215
INTER10.5761.000-0.4530.8430.211-0.4320.0730.5550.0480.6160.6790.7020.6390.0050.0040.0050.3590.3690.206
INTER2-0.120-0.4531.000-0.387-0.1400.1570.239-0.1300.1950.2630.4420.4210.9990.0230.0210.0230.2000.3300.196
INTERACTION0.6060.843-0.3871.0000.199-0.5190.0220.5850.0720.6050.5860.5590.6290.0000.0000.0000.3250.3700.173
LATITUDE0.1130.211-0.1400.1991.0000.1790.0280.0860.0730.2380.2130.1830.3650.8131.0000.8130.7630.2360.141
LONGITUDE-0.314-0.4320.157-0.5190.1791.000-0.032-0.3050.0580.3890.3340.2580.2730.1090.0450.1090.8780.3340.184
FATALITIES0.0910.0730.2390.0220.028-0.0321.0000.0700.0530.0000.0110.0060.0000.0000.0000.0000.0030.0360.023
TIMESTAMP0.7600.555-0.1300.5850.086-0.3050.0701.0000.0470.1890.2020.1990.4700.2670.3780.2670.4590.1750.127
TIME_PRECISION0.0430.0480.1950.0720.0730.0580.0530.0471.0000.1570.2890.3440.2340.0180.0260.0180.1040.0970.100
DISORDER_TYPE0.1400.6160.2630.6050.2380.3890.0000.1890.1571.0000.8131.0000.5730.0380.0450.0380.4310.3830.236
EVENT_TYPE0.1960.6790.4420.5860.2130.3340.0110.2020.2890.8131.0001.0000.5610.0380.0450.0380.3760.4260.221
SUB_EVENT_TYPE0.2200.7020.4210.5590.1830.2580.0060.1990.3441.0001.0001.0000.4310.1170.1590.1170.1940.4790.133
ACTOR20.4020.6390.9990.6290.3650.2730.0000.4700.2340.5730.5610.4311.0000.6360.5210.6360.3160.5160.227
ISO0.0000.0050.0230.0000.8130.1090.0000.2670.0180.0380.0380.1170.6361.0001.0001.0001.0000.0070.067
REGION0.0000.0040.0210.0001.0000.0450.0000.3780.0260.0450.0450.1590.5211.0001.0001.0001.0000.0030.069
COUNTRY0.0000.0050.0230.0000.8130.1090.0000.2670.0180.0380.0380.1170.6361.0001.0001.0001.0000.0070.067
ADMIN10.2460.3590.2000.3250.7630.8780.0030.4590.1040.4310.3760.1940.3161.0001.0001.0001.0000.3400.159
GEO_PRECISION0.2180.3690.3300.3700.2360.3340.0360.1750.0970.3830.4260.4790.5160.0070.0030.0070.3401.0000.355
SOURCE_SCALE0.2150.2060.1960.1730.1410.1840.0230.1270.1000.2360.2210.1330.2270.0670.0690.0670.1590.3551.000

Missing values

2023-03-29T18:15:13.583972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-29T18:15:14.430106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-29T18:15:15.471010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EVENT_ID_CNTYEVENT_DATEYEARTIME_PRECISIONDISORDER_TYPEEVENT_TYPESUB_EVENT_TYPEACTOR1ASSOC_ACTOR_1INTER1ACTOR2ASSOC_ACTOR_2INTER2INTERACTIONCIVILIAN_TARGETINGISOREGIONCOUNTRYADMIN1ADMIN2ADMIN3LOCATIONLATITUDELONGITUDEGEO_PRECISIONSOURCESOURCE_SCALENOTESFATALITIESTAGSTIMESTAMP
0ROU44820-May-201920191Political violenceViolence against civiliansAttackPolice Forces of Romania (2016-2019) Coast GuardNaN1Civilians (Turkey)Fishers (Turkey)717Civilian targeting642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9482Deschide; Hurriyet Daily; News.ro; CNN; TRT HaberNational-InternationalOn 20 May 2019, the Coast Guard of Romania fired at a Turkish fishing boat allegedly fishing in Romania's Exclusive Economic Zone (EEZ), 52 sea miles east of Costinesti [coded to Coast of Constanta]. Three Turkish fishers were reported to wounded.0NaN1649875498
1ROU188528-March-202220221Strategic developmentsStrategic developmentsDisrupted weapons useMilitary Forces of Romania (2021-)NaN1Unidentified Military ForcesNaN818NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481Adevarul; G4mediaNationalDefusal: On 28 March 2022, Romanian minesweepers conducted a controlled detonation of a sea mine 70km away from the Midia port off the Coast of Constanta. The mine was most likely strayed from a location near Ukraine.0NaN1649184809
2ROU194028-July-202220221DemonstrationsProtestsPeaceful protestProtesters (Romania)Greenpeace6NaNNaN060NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481News.roNationalOn 28 July 2022, Greenpeace activists protested in front of an oil tanker off the Coast of Constanta, demanding a stop to the dependence on fossil fuels due to their effect on the climate.0crowd size=no report1659462993
3ROU194531-July-202220221Strategic developmentsStrategic developmentsDisrupted weapons useMilitary Forces of Romania (2021-)NaN1Unidentified Armed Group (International)NaN313NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481Digi24NationalDefusal: On 31 July 2022, Romanian Naval Forces defused and destroyed a YAM sea mine off the Coast of Constanta. The mine likely came from the Northern Black Sea region from the Ukrainian coast.0NaN1660055880
4ROU194704-August-202220221DemonstrationsProtestsPeaceful protestProtesters (Romania)Greenpeace6NaNNaN060NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481News.roNationalOn 4 August 2022, Greenpeace activists protested in front of an oil tanker off the Coast of Constanta, demanding a stop to the dependence on fossil fuels due to their effect on the climate.0crowd size=no report1660055882
5ROU196108-September-202220221Political violenceExplosions/Remote violenceRemote explosive/landmine/IEDUnidentified Military ForcesNaN8Military Forces of Romania (2021-)NaN118NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481G4media; RFE/RL; Adevarul; Balkan Insight; Digi24National-InternationalOn 8 September 2022, a sea mine of unknown origin hit the DM-29 minesweeping ship of the Romanian navy 46km off the Coast of Constanta, damaging it during a demining operation. No casualties.0NaN1663096246
6ROU202610-December-202220221Strategic developmentsStrategic developmentsDisrupted weapons useMilitary Forces of Romania (2021-)NaN1Unidentified Military ForcesNaN818NaN642EuropeRomaniaConstantaNaNNaNCoast of Constanta44.15628.9481Agerpres; DeschideNational-InternationalDefusal: On 10 December 2022, the Romanian navy conducted a controlled detonation of a sea mine of unknown origin located off the Coast of Constanta.0NaN1673376402
7ROU204516-January-202320231Strategic developmentsStrategic developmentsDisrupted weapons useMilitary Forces of Romania (2021-)NaN1Military Forces of Russia (2000-)NaN818NaN642EuropeRomaniaConstantaMihai ViteazuNaNCanalul Periboina44.61128.9292Adevarul; News.roNationalDefusal: On 16 January 2023, Romanian Naval Forces captured and disabled a part of a 57E6 Russian missile that was washed away by the sea near the Canalul Periboina.0NaN1674572555
8TUR1426016-November-202020202Strategic developmentsStrategic developmentsArrestsMilitary Forces of Turkey (2016-)NaN1Civilians (Afghanistan)Refugees/IDPs (Afghanistan); Civilians (Syria); Refugees/IDPs (Syria); Civilians (Iran); Refugees/IDPs (Iran)717NaN792Middle EastTurkeySinopSinopNaNCoast of Sinop42.03935.2242Ihlas News AgencyNationalArrests: Around 16 November 2020 (as reported), 115 migrants attempting to cross the border illegally to Romania via boats were stopped off the coast of Sinop and detained by the Coast Guard Command of Turkish Army. The migrants were from Afghanistan, Syria and Iran.0NaN1606149144
9TUR1857003-November-202120211Strategic developmentsStrategic developmentsArrestsMilitary Forces of Turkey (2016-)NaN1Civilians (International)Refugees/IDPs (International)717NaN792Middle EastTurkeyIstanbulSileNaNCoast of Sile41.25329.7132Ihlas News Agency; A HaberNationalOn 3 November 2021, 40 migrants attempting to cross the border illegally on a boat were captured off the coast of Sile, Istanbul and detained by the Coast Guard Command of the Turkish army. The migrants were from Afghanistan, Iraq, Iran, Syria and Bangladesh.0NaN1636383675
EVENT_ID_CNTYEVENT_DATEYEARTIME_PRECISIONDISORDER_TYPEEVENT_TYPESUB_EVENT_TYPEACTOR1ASSOC_ACTOR_1INTER1ACTOR2ASSOC_ACTOR_2INTER2INTERACTIONCIVILIAN_TARGETINGISOREGIONCOUNTRYADMIN1ADMIN2ADMIN3LOCATIONLATITUDELONGITUDEGEO_PRECISIONSOURCESOURCE_SCALENOTESFATALITIESTAGSTIMESTAMP
96072UKR9625217-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Russia (2000-)NaN8NaNNaN080NaN804EuropeUkraineKharkivKupianskyiDvorichanskaZapadne49.82237.6142Ministry of Defence of UkraineOtherOn 17 March 2023, Russian forces shelled near Zapadne, Kharkiv. Casualties unknown.0NaN1679425924
96073UKR9625317-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Russia (2000-)NaN8NaNNaN080NaN804EuropeUkraineDonetskVolnovaskyiVelykonovosilkivskaZolota Nyva47.79436.9902Ministry of Defence of UkraineOtherOn 17 March 2023, Russian forces shelled near Zolota Nyva, Donetsk. Casualties unknown.0NaN1679425924
96074UKR9626317-March-202320231Political violenceExplosions/Remote violenceAir/drone strikeMilitary Forces of Russia (2000-) Air ForceNaN8NaNNaN080NaN804EuropeUkraineDnipropetrovskNovomoskovskyiNovomoskovskaNovomoskovsk48.63835.2462Suspilne MediaNationalOn 17 March 2023, Russian Shahed drones struck an infrastructure facility in Novomoskovsk, Dnipropetrovsk. Casualties unknown.0NaN1679425924
96075UKR9634217-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaNaN818NaN804EuropeUkraineDonetskDonetskyiDonetskaDonetsk - Kirovskyi47.96837.5481DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Donetsk - Kirovskyi, Donetsk. Casualties unknown.0NaN1679425924
96076UKR9634317-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaNaN818NaN804EuropeUkraineDonetskDonetskyiDonetskaDonetsk - Kuibyshivskyi48.02337.7281DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Donetsk - Kuibyshivskyi, Donetsk. Casualties unknown.0NaN1679425924
96077UKR9634417-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaNaN818NaN804EuropeUkraineDonetskDonetskyiDonetskaDonetsk - Kyivskyi47.98637.8621DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Donetsk - Kyivskyi, Donetsk. Casualties unknown.0NaN1679425924
96078UKR9634517-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaCivilians (Ukraine)818NaN804EuropeUkraineDonetskDonetskyiDonetskaDonetsk - Petrovskyi47.95037.6141DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Donetsk - Petrovskyi, Donetsk. Two civilians were killed.2NaN1679425924
96079UKR9634617-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaNaN818NaN804EuropeUkraineDonetskHorlivskyiHorlivskaHorlivka48.31338.0421DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Horlivka, Donetsk. Casualties unknown.0NaN1679425924
96080UKR9634717-March-202320231Political violenceExplosions/Remote violenceShelling/artillery/missile attackMilitary Forces of Ukraine (2019-)NaN1Military Forces of Russia (2000-) Donetsk People's MilitiaNaN818NaN804EuropeUkraineDonetskDonetskyiYasynuvatskaYasynuvata48.13037.8591DPR Armed Forces Press ServiceOtherOn 17 March 2023, Ukrainian forces shelled DPR in Yasynuvata, Donetsk. Casualties unknown.0NaN1679425924
96081UKR9637517-March-202320231Political violenceExplosions/Remote violenceRemote explosive/landmine/IEDYuvileine Communal Militia (Ukraine)NaN4Police Forces of Ukraine (2019-)Military Forces of Russia (2000-); Civilians (Ukraine)114NaN804EuropeUkraineKhersonKhersonskyiYuvileinaYuvileine46.48633.2111BBC NewsRegionalOn 17 March 2023, suspected partisans blew up a car of a police officer in Yuvileine, Kherson. The victim had been collaborating with Russian forces and allegedly tortured Ukrainian civilians in Nova Kakhovka. The police officer was killed, another woman was injured.1NaN1679425924